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</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="prompt_target.html">Prompt Target</a></li>
<li class="toctree-l1"><a class="reference internal" href="signals.html">Signals™</a></li>
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</ul>
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<li class="toctree-l1"><a class="reference internal" href="signals.html">Signals™</a></li>
</ul>
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</ul>
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<li class="toctree-l1"><a class="reference internal" href="signals.html">Signals™</a></li>
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</ul>
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<li class="toctree-l1"><a class="reference internal" href="../signals.html">Signals™</a></li>
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@ -108,6 +108,7 @@
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@ -438,8 +439,8 @@ that you can test and modify locally for multi-turn RAG scenarios.</p>
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<section id="signals">
<h1>Signals™<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() =&gt; $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#signals"><svg height="1em" viewbox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76 0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76 0 5-2.24 5-5s-2.24-5-5-5z"></path></svg></a></h1>
<p>Agentic Signals are behavioral and executions quality indicators that act as early warning signs of agent performance—highlighting both brilliant successes and <strong>severe failures</strong>. These signals are computed directly from conversation traces without requiring manual labeling or domain expertise, making them practical for production observability at scale.</p>
<section id="the-problem-knowing-what-s-good">
<h2>The Problem: Knowing What’s “Good”<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() =&gt; $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#the-problem-knowing-what-s-good" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#the-problem-knowing-what-s-good'"><svg height="1em" viewbox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76 0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76 0 5-2.24 5-5s-2.24-5-5-5z"></path></svg></a></h2>
<p>One of the hardest parts of building agents is measuring how well they perform in the real world.</p>
<p><strong>Offline testing</strong> relies on hand-picked examples and happy-path scenarios, missing the messy diversity of real usage. Developers manually prompt models, evaluate responses, and tune prompts by guesswork—a slow, incomplete feedback loop.</p>
<p><strong>Production debugging</strong> floods developers with traces and logs but provides little guidance on which interactions actually matter. Finding failures means painstakingly reconstructing sessions and manually labeling quality issues.</p>
<p>You can’t score every response with an LLM-as-judge (too expensive, too slow) or manually review every trace (doesn’t scale). What you need are <strong>behavioral signals</strong>—fast, economical proxies that don’t label quality outright but dramatically shrink the search space, pointing to sessions most likely to be broken or brilliant.</p>
</section>
<section id="what-are-behavioral-signals">
<h2>What Are Behavioral Signals?<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() =&gt; $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#what-are-behavioral-signals" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#what-are-behavioral-signals'"><svg height="1em" viewbox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76 0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76 0 5-2.24 5-5s-2.24-5-5-5z"></path></svg></a></h2>
<p>Behavioral signals are canaries in the coal mine—early, objective indicators that something may have gone wrong (or gone exceptionally well). They don’t explain <em>why</em> an agent failed, but they reliably signal <em>where</em> attention is needed.</p>
<p>These signals emerge naturally from the rhythm of interaction:</p>
<ul class="simple">
<li><p>A user rephrasing the same request</p></li>
<li><p>Sharp increases in conversation length</p></li>
<li><p>Frustrated follow-up messages (ALL CAPS, “this doesn’t work”, excessive !!!/???)</p></li>
<li><p>Agent repetition / looping</p></li>
<li><p>Expressions of gratitude or satisfaction</p></li>
<li><p>Requests to speak to a human / contact support</p></li>
</ul>
<p>Individually, these clues are shallow; together, they form a fingerprint of agent performance. Embedded directly into traces, they make it easy to spot friction as it happens: where users struggle, where agents loop, and where escalations occur.</p>
</section>
<section id="signals-vs-response-quality">
<h2>Signals vs Response Quality<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() =&gt; $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#signals-vs-response-quality" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#signals-vs-response-quality'"><svg height="1em" viewbox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76 0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76 0 5-2.24 5-5s-2.24-5-5-5z"></path></svg></a></h2>
<p>Behavioral signals and response quality are complementary.</p>
<dl class="simple">
<dt><strong>Response Quality</strong></dt><dd><p>Domain-specific correctness: did the agent do the right thing given business rules, user intent, and operational context? This often requires subject-matter experts or outcome instrumentation and is time-intensive but irreplaceable.</p>
</dd>
<dt><strong>Behavioral Signals</strong></dt><dd><p>Observable patterns that correlate with quality: high repair frequency, excessive turns, frustration markers, repetition, escalation, and positive feedback. Fast to compute and valuable for prioritizing which traces deserve inspection.</p>
</dd>
</dl>
<p>Used together, signals tell you <em>where to look</em>, and quality evaluation tells you <em>what went wrong (or right)</em>.</p>
</section>
<section id="how-it-works">
<h2>How It Works<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() =&gt; $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#how-it-works" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#how-it-works'"><svg height="1em" viewbox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76 0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76 0 5-2.24 5-5s-2.24-5-5-5z"></path></svg></a></h2>
<p>Signals are computed automatically by the gateway and emitted as <strong>OpenTelemetry trace attributes</strong> to your existing observability stack (Jaeger, Honeycomb, Grafana Tempo, etc.). No additional libraries or instrumentation required—just configure your OTEL collector endpoint.</p>
<p>Each conversation trace is enriched with signal attributes that you can query, filter, and visualize in your observability platform. The gateway analyzes message content (performing text normalization, Unicode handling, and pattern matching) to compute behavioral signals in real-time.</p>
<p><strong>OTEL Trace Attributes</strong></p>
<p>Signal data is exported as structured span attributes:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">signals.quality</span></code> - Overall assessment (Excellent/Good/Neutral/Poor/Severe)</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">signals.turn_count</span></code> - Total number of turns in the conversation</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">signals.efficiency_score</span></code> - Efficiency metric (0.0-1.0)</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">signals.repair.count</span></code> - Number of repair attempts detected (when present)</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">signals.repair.ratio</span></code> - Ratio of repairs to user turns (when present)</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">signals.frustration.count</span></code> - Number of frustration indicators detected</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">signals.frustration.severity</span></code> - Frustration level (0-3)</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">signals.repetition.count</span></code> - Number of repetition instances detected</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">signals.escalation.requested</span></code> - Boolean escalation flag (“true” when present)</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">signals.positive_feedback.count</span></code> - Number of positive feedback indicators</p></li>
</ul>
<p><strong>Visual Flag Marker</strong></p>
<p>When concerning signals are detected (frustration, looping, escalation, or poor/severe quality), the flag marker <strong>🚩</strong> is automatically appended to the span’s operation name, making problematic traces easy to spot in your trace visualizations.</p>
<p><strong>Querying in Your Observability Platform</strong></p>
<p>Example queries:</p>
<ul class="simple">
<li><p>Find all severe interactions: <code class="docutils literal notranslate"><span class="pre">signals.quality</span> <span class="pre">=</span> <span class="pre">"Severe"</span></code></p></li>
<li><p>Find flagged traces: search for <strong>🚩</strong> in span names</p></li>
<li><p>Find long conversations: <code class="docutils literal notranslate"><span class="pre">signals.turn_count</span> <span class="pre">&gt;</span> <span class="pre">10</span></code></p></li>
<li><p>Find inefficient interactions: <code class="docutils literal notranslate"><span class="pre">signals.efficiency_score</span> <span class="pre">&lt;</span> <span class="pre">0.5</span></code></p></li>
<li><p>Find high repair rates: <code class="docutils literal notranslate"><span class="pre">signals.repair.ratio</span> <span class="pre">&gt;</span> <span class="pre">0.3</span></code></p></li>
<li><p>Find frustrated users: <code class="docutils literal notranslate"><span class="pre">signals.frustration.severity</span> <span class="pre">&gt;=</span> <span class="pre">2</span></code></p></li>
<li><p>Find looping agents: <code class="docutils literal notranslate"><span class="pre">signals.repetition.count</span> <span class="pre">&gt;=</span> <span class="pre">3</span></code></p></li>
<li><p>Find positive interactions: <code class="docutils literal notranslate"><span class="pre">signals.positive_feedback.count</span> <span class="pre">&gt;=</span> <span class="pre">2</span></code></p></li>
<li><p>Find escalations: <code class="docutils literal notranslate"><span class="pre">signals.escalation.requested</span> <span class="pre">=</span> <span class="pre">"true"</span></code></p></li>
</ul>
<a class="reference internal image-reference" href="../_images/signals_trace.png"><img alt="../_images/signals_trace.png" class="align-center" src="../_images/signals_trace.png" style="width: 100%;"/>
</a>
</section>
<section id="core-signal-types">
<h2>Core Signal Types<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() =&gt; $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#core-signal-types" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#core-signal-types'"><svg height="1em" viewbox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76 0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76 0 5-2.24 5-5s-2.24-5-5-5z"></path></svg></a></h2>
<p>The signals system tracks six categories of behavioral indicators.</p>
<section id="turn-count-efficiency">
<h3>Turn Count &amp; Efficiency<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() =&gt; $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#turn-count-efficiency" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#turn-count-efficiency'"><svg height="1em" viewbox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76 0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76 0 5-2.24 5-5s-2.24-5-5-5z"></path></svg></a></h3>
<dl class="simple">
<dt><strong>What it measures</strong></dt><dd><p>Number of user–assistant exchanges.</p>
</dd>
<dt><strong>Why it matters</strong></dt><dd><p>Long conversations often indicate unclear intent resolution, confusion, or inefficiency. Very short conversations can correlate with crisp resolution.</p>
</dd>
</dl>
<p><strong>Key metrics</strong></p>
<ul class="simple">
<li><p>Total turn count</p></li>
<li><p>Warning thresholds (concerning: &gt;7 turns, excessive: &gt;12 turns)</p></li>
<li><p>Efficiency score (0.0–1.0)</p></li>
</ul>
<dl>
<dt><strong>Efficiency scoring</strong></dt><dd><p>Baseline expectation is ~5 turns (tunable). Efficiency stays at 1.0 up to the baseline, then declines with an inverse penalty as turns exceed baseline:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><code><span id="line-1"><span class="n">efficiency</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="mf">0.3</span> <span class="o">*</span> <span class="p">(</span><span class="n">turns</span> <span class="o">-</span> <span class="n">baseline</span><span class="p">))</span>
</span></code></pre></div>
</div>
</dd>
</dl>
</section>
<section id="follow-up-repair-frequency">
<h3>Follow-Up &amp; Repair Frequency<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() =&gt; $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#follow-up-repair-frequency" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#follow-up-repair-frequency'"><svg height="1em" viewbox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76 0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76 0 5-2.24 5-5s-2.24-5-5-5z"></path></svg></a></h3>
<dl class="simple">
<dt><strong>What it measures</strong></dt><dd><p>How often users clarify, correct, or rephrase requests. This is a <strong>user signal</strong> tracking query reformulation behavior—when users must repair or rephrase their requests because the agent didn’t understand or respond appropriately.</p>
</dd>
<dt><strong>Why it matters</strong></dt><dd><p>High repair frequency is a proxy for misunderstanding or intent drift. When users repeatedly rephrase the same request, it indicates the agent is failing to grasp or act on the user’s intent.</p>
</dd>
</dl>
<p><strong>Key metrics</strong></p>
<ul class="simple">
<li><p>Repair count and ratio (repairs / user turns)</p></li>
<li><p>Concerning threshold: &gt;30% repair ratio</p></li>
<li><p>Detected repair phrases (exact or fuzzy)</p></li>
</ul>
<p><strong>Common patterns detected</strong></p>
<ul class="simple">
<li><p>Explicit corrections: “I meant”, “correction”</p></li>
<li><p>Negations: “No, I…”, “that’s not”</p></li>
<li><p>Rephrasing: “let me rephrase”, “to clarify”</p></li>
<li><p>Mistake acknowledgment: “my mistake”, “I was wrong”</p></li>
<li><p>“Similar rephrase” heuristic based on token overlap (with stopwords downweighted)</p></li>
</ul>
</section>
<section id="user-frustration">
<h3>User Frustration<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() =&gt; $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#user-frustration" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#user-frustration'"><svg height="1em" viewbox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76 0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76 0 5-2.24 5-5s-2.24-5-5-5z"></path></svg></a></h3>
<dl class="simple">
<dt><strong>What it measures</strong></dt><dd><p>Observable frustration indicators and emotional escalation.</p>
</dd>
<dt><strong>Why it matters</strong></dt><dd><p>Catching frustration early enables intervention before users abandon or escalate.</p>
</dd>
</dl>
<p><strong>Detection patterns</strong></p>
<ul class="simple">
<li><p><strong>Complaints</strong>: “this doesn’t work”, “not helpful”, “waste of time”</p></li>
<li><p><strong>Confusion</strong>: “I don’t understand”, “makes no sense”, “I’m confused”</p></li>
<li><p><strong>Tone markers</strong>:</p>
<ul>
<li><p>ALL CAPS (&gt;=10 alphabetic chars and &gt;=80% uppercase)</p></li>
<li><p>Excessive punctuation (&gt;=3 exclamation marks or &gt;=3 question marks)</p></li>
</ul>
</li>
<li><p><strong>Profanity</strong>: token-based (avoids substring false positives like “absolute” -&gt; “bs”)</p></li>
</ul>
<p><strong>Severity levels</strong></p>
<ul class="simple">
<li><p><strong>None (0)</strong>: no indicators</p></li>
<li><p><strong>Mild (1)</strong>: 1–2 indicators</p></li>
<li><p><strong>Moderate (2)</strong>: 3–4 indicators</p></li>
<li><p><strong>Severe (3)</strong>: 5+ indicators</p></li>
</ul>
</section>
<section id="repetition-looping">
<h3>Repetition &amp; Looping<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() =&gt; $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#repetition-looping" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#repetition-looping'"><svg height="1em" viewbox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76 0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76 0 5-2.24 5-5s-2.24-5-5-5z"></path></svg></a></h3>
<dl class="simple">
<dt><strong>What it measures</strong></dt><dd><p>Assistant repetition / degenerative loops. This is an <strong>assistant signal</strong> tracking when the agent repeats itself, fails to follow instructions, or gets stuck in loops—indicating the agent is not making progress or adapting its responses.</p>
</dd>
<dt><strong>Why it matters</strong></dt><dd><p>Often indicates missing state tracking, broken tool integration, prompt issues, or the agent ignoring user corrections. High repetition means the agent is not learning from the conversation context.</p>
</dd>
</dl>
<p><strong>Detection method</strong></p>
<ul class="simple">
<li><p>Compare assistant messages using <strong>bigram Jaccard similarity</strong></p></li>
<li><p>Classify:</p>
<ul>
<li><p><strong>Exact</strong>: similarity &gt;= 0.85</p></li>
<li><p><strong>Near-duplicate</strong>: similarity &gt;= 0.50</p></li>
</ul>
</li>
<li><p>Looping is flagged when repetition instances exceed 2 in a session.</p></li>
</ul>
<p><strong>Severity levels</strong></p>
<ul class="simple">
<li><p><strong>None (0)</strong>: 0 instances</p></li>
<li><p><strong>Mild (1)</strong>: 1–2 instances</p></li>
<li><p><strong>Moderate (2)</strong>: 3–4 instances</p></li>
<li><p><strong>Severe (3)</strong>: 5+ instances</p></li>
</ul>
</section>
<section id="positive-feedback">
<h3>Positive Feedback<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() =&gt; $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#positive-feedback" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#positive-feedback'"><svg height="1em" viewbox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76 0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76 0 5-2.24 5-5s-2.24-5-5-5z"></path></svg></a></h3>
<dl class="simple">
<dt><strong>What it measures</strong></dt><dd><p>User expressions of satisfaction, gratitude, and success.</p>
</dd>
<dt><strong>Why it matters</strong></dt><dd><p>Strong positive signals identify exemplar traces for prompt engineering and evaluation.</p>
</dd>
</dl>
<p><strong>Detection patterns</strong></p>
<ul class="simple">
<li><p>Gratitude: “thank you”, “appreciate it”</p></li>
<li><p>Satisfaction: “that’s great”, “awesome”, “love it”</p></li>
<li><p>Success confirmation: “got it”, “that worked”, “perfect”</p></li>
</ul>
<p><strong>Confidence scoring</strong></p>
<ul class="simple">
<li><p>1 indicator: 0.6</p></li>
<li><p>2 indicators: 0.8</p></li>
<li><p>3+ indicators: 0.95</p></li>
</ul>
</section>
<section id="escalation-requests">
<h3>Escalation Requests<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() =&gt; $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#escalation-requests" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#escalation-requests'"><svg height="1em" viewbox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76 0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76 0 5-2.24 5-5s-2.24-5-5-5z"></path></svg></a></h3>
<dl class="simple">
<dt><strong>What it measures</strong></dt><dd><p>Requests for human help/support or threats to quit.</p>
</dd>
<dt><strong>Why it matters</strong></dt><dd><p>Escalation is a strong signal that the agent failed to resolve the interaction.</p>
</dd>
</dl>
<p><strong>Detection patterns</strong></p>
<ul class="simple">
<li><p>Human requests: “speak to a human”, “real person”, “live agent”</p></li>
<li><p>Support: “contact support”, “customer service”, “help desk”</p></li>
<li><p>Quit threats: “I’m done”, “forget it”, “I give up”</p></li>
</ul>
</section>
</section>
<section id="overall-quality-assessment">
<h2>Overall Quality Assessment<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() =&gt; $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#overall-quality-assessment" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#overall-quality-assessment'"><svg height="1em" viewbox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76 0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76 0 5-2.24 5-5s-2.24-5-5-5z"></path></svg></a></h2>
<p>Signals are aggregated into an overall interaction quality on a 5-point scale.</p>
<dl class="simple">
<dt><strong>Excellent</strong></dt><dd><p>Strong positive signals, efficient resolution, low friction.</p>
</dd>
<dt><strong>Good</strong></dt><dd><p>Mostly positive with minor clarifications; some back-and-forth but successful.</p>
</dd>
<dt><strong>Neutral</strong></dt><dd><p>Mixed signals; neither clearly good nor bad.</p>
</dd>
<dt><strong>Poor</strong></dt><dd><p>Concerning negative patterns (high friction, multiple repairs, moderate frustration). High abandonment risk.</p>
</dd>
<dt><strong>Severe</strong></dt><dd><p>Critical issues—escalation requested, severe frustration, severe looping, or excessive turns (&gt;12). Requires immediate attention.</p>
</dd>
</dl>
<p>This assessment uses a scoring model that weighs positive factors (efficiency, positive feedback) against negative ones (frustration, repairs, repetition, escalation).</p>
</section>
<section id="sampling-and-prioritization">
<h2>Sampling and Prioritization<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() =&gt; $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#sampling-and-prioritization" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#sampling-and-prioritization'"><svg height="1em" viewbox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76 0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76 0 5-2.24 5-5s-2.24-5-5-5z"></path></svg></a></h2>
<p>In production, trace data is overwhelming. Signals provide a lightweight first layer of analysis to prioritize which sessions deserve review.</p>
<p>Workflow:</p>
<ol class="arabic simple">
<li><p>Gateway captures conversation messages and computes signals</p></li>
<li><p>Signal attributes are emitted to OTEL spans automatically</p></li>
<li><p>Your observability platform ingests and indexes the attributes</p></li>
<li><p>Query/filter by signal attributes to surface outliers (poor/severe and exemplars)</p></li>
<li><p>Review high-information traces to identify improvement opportunities</p></li>
<li><p>Update prompts, routing, or policies based on findings</p></li>
<li><p>Redeploy and monitor signal metrics to validate improvements</p></li>
</ol>
<p>This creates a reinforcement loop where traces become both diagnostic data and training signal.</p>
</section>
<section id="trace-filtering-and-telemetry">
<h2>Trace Filtering and Telemetry<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() =&gt; $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#trace-filtering-and-telemetry" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#trace-filtering-and-telemetry'"><svg height="1em" viewbox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76 0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76 0 5-2.24 5-5s-2.24-5-5-5z"></path></svg></a></h2>
<p>Signal attributes are automatically added to OpenTelemetry spans, making them immediately queryable in your observability platform.</p>
<p><strong>Visual Filtering</strong></p>
<p>When concerning signals are detected, the flag marker <strong>🚩</strong> (U+1F6A9) is automatically appended to the span’s operation name. This makes flagged sessions immediately visible in trace visualizations without requiring attribute filtering.</p>
<p><strong>Example Span Attributes</strong>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><code><span id="line-1"><span class="c1"># Span name: "POST /v1/chat/completions gpt-4 🚩"</span>
</span><span id="line-2"><span class="n">signals</span><span class="o">.</span><span class="n">quality</span> <span class="o">=</span> <span class="s2">"Severe"</span>
</span><span id="line-3"><span class="n">signals</span><span class="o">.</span><span class="n">turn_count</span> <span class="o">=</span> <span class="mi">15</span>
</span><span id="line-4"><span class="n">signals</span><span class="o">.</span><span class="n">efficiency_score</span> <span class="o">=</span> <span class="mf">0.234</span>
</span><span id="line-5"><span class="n">signals</span><span class="o">.</span><span class="n">repair</span><span class="o">.</span><span class="n">count</span> <span class="o">=</span> <span class="mi">4</span>
</span><span id="line-6"><span class="n">signals</span><span class="o">.</span><span class="n">repair</span><span class="o">.</span><span class="n">ratio</span> <span class="o">=</span> <span class="mf">0.571</span>
</span><span id="line-7"><span class="n">signals</span><span class="o">.</span><span class="n">frustration</span><span class="o">.</span><span class="n">severity</span> <span class="o">=</span> <span class="mi">3</span>
</span><span id="line-8"><span class="n">signals</span><span class="o">.</span><span class="n">frustration</span><span class="o">.</span><span class="n">count</span> <span class="o">=</span> <span class="mi">5</span>
</span><span id="line-9"><span class="n">signals</span><span class="o">.</span><span class="n">escalation</span><span class="o">.</span><span class="n">requested</span> <span class="o">=</span> <span class="s2">"true"</span>
</span><span id="line-10"><span class="n">signals</span><span class="o">.</span><span class="n">repetition</span><span class="o">.</span><span class="n">count</span> <span class="o">=</span> <span class="mi">4</span>
</span></code></pre></div>
</div>
<p><strong>Building Dashboards</strong></p>
<p>Use signal attributes to build monitoring dashboards in Grafana, Honeycomb, Datadog, etc.:</p>
<ul class="simple">
<li><p><strong>Quality distribution</strong>: Count of traces by <code class="docutils literal notranslate"><span class="pre">signals.quality</span></code></p></li>
<li><p><strong>P95 turn count</strong>: 95th percentile of <code class="docutils literal notranslate"><span class="pre">signals.turn_count</span></code></p></li>
<li><p><strong>Average efficiency</strong>: Mean of <code class="docutils literal notranslate"><span class="pre">signals.efficiency_score</span></code></p></li>
<li><p><strong>High repair rate</strong>: Percentage where <code class="docutils literal notranslate"><span class="pre">signals.repair.ratio</span> <span class="pre">&gt;</span> <span class="pre">0.3</span></code></p></li>
<li><p><strong>Frustration rate</strong>: Percentage where <code class="docutils literal notranslate"><span class="pre">signals.frustration.severity</span> <span class="pre">&gt;=</span> <span class="pre">2</span></code></p></li>
<li><p><strong>Escalation rate</strong>: Percentage where <code class="docutils literal notranslate"><span class="pre">signals.escalation.requested</span> <span class="pre">=</span> <span class="pre">"true"</span></code></p></li>
<li><p><strong>Looping rate</strong>: Percentage where <code class="docutils literal notranslate"><span class="pre">signals.repetition.count</span> <span class="pre">&gt;=</span> <span class="pre">3</span></code></p></li>
<li><p><strong>Positive feedback rate</strong>: Percentage where <code class="docutils literal notranslate"><span class="pre">signals.positive_feedback.count</span> <span class="pre">&gt;=</span> <span class="pre">1</span></code></p></li>
</ul>
<p><strong>Creating Alerts</strong></p>
<p>Set up alerts based on signal thresholds:</p>
<ul class="simple">
<li><p>Alert when severe interaction count exceeds threshold in 1-hour window</p></li>
<li><p>Alert on sudden spike in frustration rate (&gt;2x baseline)</p></li>
<li><p>Alert when escalation rate exceeds 5% of total conversations</p></li>
<li><p>Alert on degraded efficiency (P95 turn count increases &gt;50%)</p></li>
</ul>
</section>
<section id="best-practices">
<h2>Best Practices<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() =&gt; $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#best-practices" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#best-practices'"><svg height="1em" viewbox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76 0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76 0 5-2.24 5-5s-2.24-5-5-5z"></path></svg></a></h2>
<p>Start simple:</p>
<ul class="simple">
<li><p>Alert or page on <strong>Severe</strong> sessions (or on spikes in Severe rate)</p></li>
<li><p>Review <strong>Poor</strong> sessions within 24 hours</p></li>
<li><p>Sample <strong>Excellent</strong> sessions as exemplars</p></li>
</ul>
<p>Combine multiple signals to infer failure modes:</p>
<ul class="simple">
<li><p>Looping: repetition severity &gt;= 2 + excessive turns</p></li>
<li><p>User giving up: frustration severity &gt;= 2 + escalation requested</p></li>
<li><p>Misunderstood intent: repair ratio &gt; 30% + excessive turns</p></li>
<li><p>Working well: positive feedback + high efficiency + no frustration</p></li>
</ul>
</section>
<section id="limitations-and-considerations">
<h2>Limitations and Considerations<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() =&gt; $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#limitations-and-considerations" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#limitations-and-considerations'"><svg height="1em" viewbox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76 0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76 0 5-2.24 5-5s-2.24-5-5-5z"></path></svg></a></h2>
<p>Signals don’t capture:</p>
<ul class="simple">
<li><p>Task completion / real outcomes</p></li>
<li><p>Factual or domain correctness</p></li>
<li><p>Silent abandonment (user leaves without expressing frustration)</p></li>
<li><p>Non-English nuance (pattern libraries are English-oriented)</p></li>
</ul>
<p>Mitigation strategies:</p>
<ul class="simple">
<li><p>Periodically sample flagged sessions and measure false positives/negatives</p></li>
<li><p>Tune baselines per use case and user population</p></li>
<li><p>Add domain-specific phrase libraries where needed</p></li>
<li><p>Combine signals with non-text metrics (tool failures, disconnects, latency)</p></li>
</ul>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Behavioral signals complement—but do not replace—domain-specific response quality evaluation. Use signals to prioritize which traces to inspect, then apply domain expertise and outcome checks to diagnose root causes.</p>
</div>
<div class="admonition tip">
<p class="admonition-title">Tip</p>
<p>The flag marker in the span name provides instant visual feedback in trace UIs, while the structured attributes (<code class="docutils literal notranslate"><span class="pre">signals.quality</span></code>, <code class="docutils literal notranslate"><span class="pre">signals.frustration.severity</span></code>, etc.) enable powerful querying and aggregation in your observability platform.</p>
</div>
</section>
<section id="see-also">
<h2>See Also<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() =&gt; $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#see-also" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#see-also'"><svg height="1em" viewbox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76 0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76 0 5-2.24 5-5s-2.24-5-5-5z"></path></svg></a></h2>
<ul class="simple">
<li><p><a class="reference internal" href="../guides/observability/tracing.html"><span class="doc">Tracing</span></a> - Distributed tracing for agent systems</p></li>
<li><p><a class="reference internal" href="../guides/observability/monitoring.html"><span class="doc">Monitoring</span></a> - Metrics and dashboards</p></li>
<li><p><a class="reference internal" href="../guides/observability/access_logging.html"><span class="doc">Access Logging</span></a> - Request/response logging</p></li>
<li><p><a class="reference internal" href="../guides/observability/observability.html"><span class="doc">Observability</span></a> - Complete observability guide</p></li>
</ul>
</section>
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<ul>
<li><a :data-current="activeSection === '#the-problem-knowing-what-s-good'" class="reference internal" href="#the-problem-knowing-what-s-good">The Problem: Knowing What’s “Good”</a></li>
<li><a :data-current="activeSection === '#what-are-behavioral-signals'" class="reference internal" href="#what-are-behavioral-signals">What Are Behavioral Signals?</a></li>
<li><a :data-current="activeSection === '#signals-vs-response-quality'" class="reference internal" href="#signals-vs-response-quality">Signals vs Response Quality</a></li>
<li><a :data-current="activeSection === '#how-it-works'" class="reference internal" href="#how-it-works">How It Works</a></li>
<li><a :data-current="activeSection === '#core-signal-types'" class="reference internal" href="#core-signal-types">Core Signal Types</a><ul>
<li><a :data-current="activeSection === '#turn-count-efficiency'" class="reference internal" href="#turn-count-efficiency">Turn Count &amp; Efficiency</a></li>
<li><a :data-current="activeSection === '#follow-up-repair-frequency'" class="reference internal" href="#follow-up-repair-frequency">Follow-Up &amp; Repair Frequency</a></li>
<li><a :data-current="activeSection === '#user-frustration'" class="reference internal" href="#user-frustration">User Frustration</a></li>
<li><a :data-current="activeSection === '#repetition-looping'" class="reference internal" href="#repetition-looping">Repetition &amp; Looping</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../concepts/signals.html">Signals™</a></li>
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<div id="content" role="main">
<section id="overview">
<span id="id1"></span><h1>Overview<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() =&gt; $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#overview"><svg height="1em" viewbox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76 0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76 0 5-2.24 5-5s-2.24-5-5-5z"></path></svg></a></h1>
<p><a class="reference external" href="https://github.com/katanemo/plano" rel="nofollow noopener">Plano<svg fill="currentColor" height="1em" stroke="none" viewbox="0 96 960 960" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M188 868q-11-11-11-28t11-28l436-436H400q-17 0-28.5-11.5T360 336q0-17 11.5-28.5T400 296h320q17 0 28.5 11.5T760 336v320q0 17-11.5 28.5T720 696q-17 0-28.5-11.5T680 656V432L244 868q-11 11-28 11t-28-11Z"></path></svg></a> is delivery infrastructure for agentic apps. A models-native proxy server and data plane designed to help you build agents faster, and deliver them reliably to production.</p>
<p><a class="reference external" href="https://github.com/katanemo/plano" rel="nofollow noopener">Plano<svg fill="currentColor" height="1em" stroke="none" viewbox="0 96 960 960" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M188 868q-11-11-11-28t11-28l436-436H400q-17 0-28.5-11.5T360 336q0-17 11.5-28.5T400 296h320q17 0 28.5 11.5T760 336v320q0 17-11.5 28.5T720 696q-17 0-28.5-11.5T680 656V432L244 868q-11 11-28 11t-28-11Z"></path></svg></a> is delivery infrastructure for agentic apps. A smart proxy server and data plane designed to help you build agents faster, and deliver them reliably to production.</p>
<p>Plano pulls out the rote plumbing work (the “hidden AI middleware”) and decouples you from brittle, ever‑changing framework abstractions. It centralizes what shouldn’t be bespoke in every codebase like agent routing and orchestration, rich agentic signals and traces for continuous improvement, guardrail filters for safety and moderation, and smart LLM routing APIs for UX and DX agility. Use any language or AI framework, and ship agents to production faster with Plano.</p>
<p>Built by core contributors to the widely adopted <a class="reference external" href="https://www.envoyproxy.io/" rel="nofollow noopener">Envoy Proxy<svg fill="currentColor" height="1em" stroke="none" viewbox="0 96 960 960" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M188 868q-11-11-11-28t11-28l436-436H400q-17 0-28.5-11.5T360 336q0-17 11.5-28.5T400 296h320q17 0 28.5 11.5T760 336v320q0 17-11.5 28.5T720 696q-17 0-28.5-11.5T680 656V432L244 868q-11 11-28 11t-28-11Z"></path></svg></a>, Plano gives you a production‑grade foundation for agentic applications. It helps <strong>developers</strong> stay focused on the core logic of their agents, helps <strong>product teams</strong> shorten feedback loops for learning, and helps <strong>engineering teams</strong> standardize policy and safety across agents and LLMs. Plano is grounded in open protocols (de facto: OpenAI‑style v1/responses, de jure: MCP) and proven patterns like sidecar deployments, so it plugs in cleanly while remaining robust, scalable, and flexible.</p>
<p>In this documentation, you’ll learn how to set up Plano quickly, trigger API calls via prompts, apply guardrails without tight coupling with application code, simplify model and provider integration, and improve observability — so that you can focus on what matters most: the core product logic of your agents.</p>

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<li class="toctree-l1"><a class="reference internal" href="../concepts/prompt_target.html">Prompt Target</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../../concepts/prompt_target.html">Prompt Target</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../concepts/signals.html">Signals™</a></li>
</ul>
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<ul>
<li class="toctree-l1"><a class="reference internal" href="tracing.html">Tracing</a><ul>
<li class="toctree-l2"><a class="reference internal" href="tracing.html#overview">Overview</a></li>
<li class="toctree-l2"><a class="reference internal" href="tracing.html#understanding-plano-traces">Understanding Plano Traces</a></li>
<li class="toctree-l2"><a class="reference internal" href="tracing.html#behavioral-signals-in-traces">Behavioral Signals in Traces</a></li>
<li class="toctree-l2"><a class="reference internal" href="tracing.html#benefits-of-using-traceparent-headers">Benefits of Using <code class="docutils literal notranslate"><span class="pre">Traceparent</span></code> Headers</a></li>
<li class="toctree-l2"><a class="reference internal" href="tracing.html#how-to-initiate-a-trace">How to Initiate A Trace</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../../concepts/prompt_target.html">Prompt Target</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../concepts/signals.html">Signals™</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Guides</span></p>
<ul class="current">
@ -178,6 +179,111 @@ tools.</p>
<a class="reference internal image-reference" href="../../_images/tracing.png"><img alt="../../_images/tracing.png" class="align-center" src="../../_images/tracing.png" style="width: 100%;"/>
</a>
</section>
<section id="understanding-plano-traces">
<h2>Understanding Plano Traces<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() =&gt; $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#understanding-plano-traces" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#understanding-plano-traces'"><svg height="1em" viewbox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76 0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76 0 5-2.24 5-5s-2.24-5-5-5z"></path></svg></a></h2>
<p>Plano creates structured traces that capture the complete flow of requests through your AI system. Each trace consists of multiple spans representing different stages of processing.</p>
<section id="inbound-request-handling">
<h3>Inbound Request Handling<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() =&gt; $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#inbound-request-handling" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#inbound-request-handling'"><svg height="1em" viewbox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76 0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76 0 5-2.24 5-5s-2.24-5-5-5z"></path></svg></a></h3>
<p>When a request enters Plano, it creates an <strong>inbound span</strong> (<code class="docutils literal notranslate"><span class="pre">plano(inbound)</span></code>) that represents the initial request reception and processing. This span captures:</p>
<ul class="simple">
<li><p>HTTP request details (method, path, headers)</p></li>
<li><p>Request payload size</p></li>
<li><p>Initial validation and authentication</p></li>
</ul>
</section>
<section id="orchestration-routing">
<h3>Orchestration &amp; Routing<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() =&gt; $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#orchestration-routing" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#orchestration-routing'"><svg height="1em" viewbox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76 0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76 0 5-2.24 5-5s-2.24-5-5-5z"></path></svg></a></h3>
<p>For agent systems, Plano performs intelligent routing through orchestration spans:</p>
<ul class="simple">
<li><p><strong>Agent Orchestration</strong> (<code class="docutils literal notranslate"><span class="pre">plano(orchestrator)</span></code>): When multiple agents are available, Plano uses an LLM to analyze the user’s intent and select the most appropriate agent. This span captures the orchestration decision-making process.</p></li>
<li><p><strong>LLM Routing</strong> (<code class="docutils literal notranslate"><span class="pre">plano(routing)</span></code>): For direct LLM requests, Plano determines the optimal endpoint based on your routing strategy (round-robin, least-latency, cost-optimized). This span includes:</p>
<ul>
<li><p>Routing strategy used</p></li>
<li><p>Selected upstream endpoint</p></li>
<li><p>Route determination time</p></li>
<li><p>Fallback indicators (if applicable)</p></li>
</ul>
</li>
</ul>
</section>
<section id="agent-processing">
<h3>Agent Processing<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() =&gt; $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#agent-processing" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#agent-processing'"><svg height="1em" viewbox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76 0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76 0 5-2.24 5-5s-2.24-5-5-5z"></path></svg></a></h3>
<p>When requests are routed to agents, Plano creates spans for agent execution:</p>
<ul class="simple">
<li><p><strong>Agent Filter Chains</strong> (<code class="docutils literal notranslate"><span class="pre">plano(filter)</span></code>): If filters are configured (guardrails, context enrichment, query rewriting), each filter execution is captured in its own span, showing the transformation pipeline.</p></li>
<li><p><strong>Agent Execution</strong> (<code class="docutils literal notranslate"><span class="pre">plano(agent)</span></code>): The main agent processing span that captures the agent’s work, including any tools invoked and intermediate reasoning steps.</p></li>
</ul>
</section>
<section id="outbound-llm-calls">
<h3>Outbound LLM Calls<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() =&gt; $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#outbound-llm-calls" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#outbound-llm-calls'"><svg height="1em" viewbox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76 0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76 0 5-2.24 5-5s-2.24-5-5-5z"></path></svg></a></h3>
<p>All LLM calls—whether from Plano’s routing layer or from agents—are traced with <strong>LLM spans</strong> (<code class="docutils literal notranslate"><span class="pre">plano(llm)</span></code>) that capture:</p>
<ul class="simple">
<li><p>Model name and provider (e.g., <code class="docutils literal notranslate"><span class="pre">gpt-4</span></code>, <code class="docutils literal notranslate"><span class="pre">claude-3-sonnet</span></code>)</p></li>
<li><p>Request parameters (temperature, max_tokens, top_p)</p></li>
<li><p>Token usage (prompt_tokens, completion_tokens)</p></li>
<li><p>Streaming indicators and time-to-first-token</p></li>
<li><p>Response metadata</p></li>
</ul>
<p><strong>Example Span Attributes</strong>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><code><span id="line-1"><span class="c1"># LLM call span</span>
</span><span id="line-2"><span class="n">llm</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="s2">"gpt-4"</span>
</span><span id="line-3"><span class="n">llm</span><span class="o">.</span><span class="n">provider</span> <span class="o">=</span> <span class="s2">"openai"</span>
</span><span id="line-4"><span class="n">llm</span><span class="o">.</span><span class="n">usage</span><span class="o">.</span><span class="n">prompt_tokens</span> <span class="o">=</span> <span class="mi">150</span>
</span><span id="line-5"><span class="n">llm</span><span class="o">.</span><span class="n">usage</span><span class="o">.</span><span class="n">completion_tokens</span> <span class="o">=</span> <span class="mi">75</span>
</span><span id="line-6"><span class="n">llm</span><span class="o">.</span><span class="n">duration_ms</span> <span class="o">=</span> <span class="mi">1250</span>
</span><span id="line-7"><span class="n">llm</span><span class="o">.</span><span class="n">time_to_first_token</span> <span class="o">=</span> <span class="mi">320</span>
</span></code></pre></div>
</div>
</section>
<section id="handoff-to-upstream-services">
<h3>Handoff to Upstream Services<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() =&gt; $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#handoff-to-upstream-services" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#handoff-to-upstream-services'"><svg height="1em" viewbox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76 0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76 0 5-2.24 5-5s-2.24-5-5-5z"></path></svg></a></h3>
<p>When Plano forwards requests to upstream services (agents, APIs, or LLM providers), it creates <strong>handoff spans</strong> (<code class="docutils literal notranslate"><span class="pre">plano(handoff)</span></code>) that capture:</p>
<ul class="simple">
<li><p>Upstream endpoint URL</p></li>
<li><p>Request/response sizes</p></li>
<li><p>HTTP status codes</p></li>
<li><p>Upstream response times</p></li>
</ul>
<p>This creates a complete end-to-end trace showing the full request lifecycle through all system components.</p>
</section>
</section>
<section id="behavioral-signals-in-traces">
<h2>Behavioral Signals in Traces<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() =&gt; $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#behavioral-signals-in-traces" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#behavioral-signals-in-traces'"><svg height="1em" viewbox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76 0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76 0 5-2.24 5-5s-2.24-5-5-5z"></path></svg></a></h2>
<p>Plano automatically enriches OpenTelemetry traces with <a class="reference internal" href="../../concepts/signals.html"><span class="doc">Signals™</span></a> — behavioral quality indicators computed from conversation patterns. These signals are attached as span attributes, providing immediate visibility into interaction quality.</p>
<p><strong>What Signals Provide</strong></p>
<p>Signals act as early warning indicators embedded in your traces:</p>
<ul class="simple">
<li><p><strong>Quality Assessment</strong>: Overall interaction quality (Excellent/Good/Neutral/Poor/Severe)</p></li>
<li><p><strong>Efficiency Metrics</strong>: Turn count, efficiency scores, repair frequency</p></li>
<li><p><strong>User Sentiment</strong>: Frustration indicators, positive feedback, escalation requests</p></li>
<li><p><strong>Agent Behavior</strong>: Repetition detection, looping patterns</p></li>
</ul>
<p><strong>Visual Flag Markers</strong></p>
<p>When concerning signals are detected (frustration, looping, escalation, or poor/severe quality), Plano automatically appends a flag marker <strong>🚩</strong> to the span’s operation name. This makes problematic traces immediately visible in your tracing UI without requiring additional queries.</p>
<p><strong>Example Span with Signals</strong>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><code><span id="line-1"><span class="c1"># Span name: "POST /v1/chat/completions gpt-4 🚩"</span>
</span><span id="line-2"><span class="c1"># Standard LLM attributes:</span>
</span><span id="line-3"><span class="n">llm</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="s2">"gpt-4"</span>
</span><span id="line-4"><span class="n">llm</span><span class="o">.</span><span class="n">usage</span><span class="o">.</span><span class="n">total_tokens</span> <span class="o">=</span> <span class="mi">225</span>
</span><span id="line-5">
</span><span id="line-6"><span class="c1"># Behavioral signal attributes:</span>
</span><span id="line-7"><span class="n">signals</span><span class="o">.</span><span class="n">quality</span> <span class="o">=</span> <span class="s2">"Severe"</span>
</span><span id="line-8"><span class="n">signals</span><span class="o">.</span><span class="n">turn_count</span> <span class="o">=</span> <span class="mi">15</span>
</span><span id="line-9"><span class="n">signals</span><span class="o">.</span><span class="n">efficiency_score</span> <span class="o">=</span> <span class="mf">0.234</span>
</span><span id="line-10"><span class="n">signals</span><span class="o">.</span><span class="n">frustration</span><span class="o">.</span><span class="n">severity</span> <span class="o">=</span> <span class="mi">3</span>
</span><span id="line-11"><span class="n">signals</span><span class="o">.</span><span class="n">escalation</span><span class="o">.</span><span class="n">requested</span> <span class="o">=</span> <span class="s2">"true"</span>
</span></code></pre></div>
</div>
<p><strong>Querying Signal Data</strong></p>
<p>In your observability platform (Jaeger, Grafana Tempo, Datadog, etc.), filter traces by signal attributes:</p>
<ul class="simple">
<li><p>Find severe interactions: <code class="docutils literal notranslate"><span class="pre">signals.quality</span> <span class="pre">=</span> <span class="pre">"Severe"</span></code></p></li>
<li><p>Find frustrated users: <code class="docutils literal notranslate"><span class="pre">signals.frustration.severity</span> <span class="pre">&gt;=</span> <span class="pre">2</span></code></p></li>
<li><p>Find inefficient flows: <code class="docutils literal notranslate"><span class="pre">signals.efficiency_score</span> <span class="pre">&lt;</span> <span class="pre">0.5</span></code></p></li>
<li><p>Find escalations: <code class="docutils literal notranslate"><span class="pre">signals.escalation.requested</span> <span class="pre">=</span> <span class="pre">"true"</span></code></p></li>
</ul>
<p>For complete details on all available signals, detection methods, and best practices, see the <a class="reference internal" href="../../concepts/signals.html"><span class="doc">Signals™</span></a> guide.</p>
</section>
<section id="benefits-of-using-traceparent-headers">
<h2>Benefits of Using <code class="docutils literal notranslate"><span class="pre">Traceparent</span></code> Headers<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() =&gt; $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#benefits-of-using-traceparent-headers" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#benefits-of-using-traceparent-headers'"><svg height="1em" viewbox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76 0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76 0 5-2.24 5-5s-2.24-5-5-5z"></path></svg></a></h2>
<ul class="simple">
@ -459,6 +565,15 @@ tools like AWS X-Ray and Datadog, enhancing observability and facilitating faste
<div class="sticky top-16 -mt-10 max-h-[calc(100vh-5rem)] overflow-y-auto pt-6 space-y-2"><p class="font-medium">On this page</p>
<ul>
<li><a :data-current="activeSection === '#overview'" class="reference internal" href="#overview">Overview</a></li>
<li><a :data-current="activeSection === '#understanding-plano-traces'" class="reference internal" href="#understanding-plano-traces">Understanding Plano Traces</a><ul>
<li><a :data-current="activeSection === '#inbound-request-handling'" class="reference internal" href="#inbound-request-handling">Inbound Request Handling</a></li>
<li><a :data-current="activeSection === '#orchestration-routing'" class="reference internal" href="#orchestration-routing">Orchestration &amp; Routing</a></li>
<li><a :data-current="activeSection === '#agent-processing'" class="reference internal" href="#agent-processing">Agent Processing</a></li>
<li><a :data-current="activeSection === '#outbound-llm-calls'" class="reference internal" href="#outbound-llm-calls">Outbound LLM Calls</a></li>
<li><a :data-current="activeSection === '#handoff-to-upstream-services'" class="reference internal" href="#handoff-to-upstream-services">Handoff to Upstream Services</a></li>
</ul>
</li>
<li><a :data-current="activeSection === '#behavioral-signals-in-traces'" class="reference internal" href="#behavioral-signals-in-traces">Behavioral Signals in Traces</a></li>
<li><a :data-current="activeSection === '#benefits-of-using-traceparent-headers'" class="reference internal" href="#benefits-of-using-traceparent-headers">Benefits of Using <code class="docutils literal notranslate"><span class="pre">Traceparent</span></code> Headers</a></li>
<li><a :data-current="activeSection === '#how-to-initiate-a-trace'" class="reference internal" href="#how-to-initiate-a-trace">How to Initiate A Trace</a></li>
<li><a :data-current="activeSection === '#trace-propagation'" class="reference internal" href="#trace-propagation">Trace Propagation</a><ul>

7
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@ -19,7 +19,7 @@
<link href="../_static/favicon.ico" rel="icon"/>
<link href="../search.html" rel="search" title="Search"/>
<link href="llm_router.html" rel="next" title="LLM Routing"/>
<link href="../concepts/prompt_target.html" rel="prev" title="Prompt Target"/>
<link href="../concepts/signals.html" rel="prev" title="Signals™"/>
<script>
<!-- Prevent Flash of wrong theme -->
const userPreference = localStorage.getItem('darkMode');
@ -108,6 +108,7 @@
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../concepts/prompt_target.html">Prompt Target</a></li>
<li class="toctree-l1"><a class="reference internal" href="../concepts/signals.html">Signals™</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Guides</span></p>
<ul class="current">
@ -886,11 +887,11 @@ Plano makes it easy to build and scale these systems by managing the orchestrati
</section>
</div><div class="flex justify-between items-center pt-6 mt-12 border-t border-border gap-4">
<div class="mr-auto">
<a class="inline-flex items-center justify-center rounded-md text-sm font-medium transition-colors border border-input hover:bg-accent hover:text-accent-foreground py-2 px-4" href="../concepts/prompt_target.html">
<a class="inline-flex items-center justify-center rounded-md text-sm font-medium transition-colors border border-input hover:bg-accent hover:text-accent-foreground py-2 px-4" href="../concepts/signals.html">
<svg class="mr-2 h-4 w-4" fill="none" height="24" stroke="currentColor" stroke-linecap="round" stroke-linejoin="round" stroke-width="2" viewbox="0 0 24 24" width="24" xmlns="http://www.w3.org/2000/svg">
<polyline points="15 18 9 12 15 6"></polyline>
</svg>
Prompt Target
Signals™
</a>
</div>
<div class="ml-auto">

1
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@ -108,6 +108,7 @@
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../concepts/prompt_target.html">Prompt Target</a></li>
<li class="toctree-l1"><a class="reference internal" href="../concepts/signals.html">Signals™</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Guides</span></p>
<ul class="current">

1
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@ -108,6 +108,7 @@
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../concepts/prompt_target.html">Prompt Target</a></li>
<li class="toctree-l1"><a class="reference internal" href="../concepts/signals.html">Signals™</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Guides</span></p>
<ul class="current">

570
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@ -1,6 +1,6 @@
Plano Docs v0.4.1
llms.txt (auto-generated)
Generated (UTC): 2026-01-07T16:45:10.576143+00:00
Generated (UTC): 2026-01-07T19:21:15.065859+00:00
Table of contents
- Agents (concepts/agents)
@ -11,6 +11,7 @@ Table of contents
- Model Aliases (concepts/llm_providers/model_aliases)
- Supported Providers & Configuration (concepts/llm_providers/supported_providers)
- Prompt Target (concepts/prompt_target)
- Signals™ (concepts/signals)
- Intro to Plano (get_started/intro_to_plano)
- Overview (get_started/overview)
- Quickstart (get_started/quickstart)
@ -2277,6 +2278,445 @@ By carefully designing prompt targets as deterministic, task-specific entry poin
---
Signals™
--------
Doc: concepts/signals
-*- coding: utf-8 -*-
Signals™
Agentic Signals are behavioral and executions quality indicators that act as early warning signs of agent performance—highlighting both brilliant successes and severe failures. These signals are computed directly from conversation traces without requiring manual labeling or domain expertise, making them practical for production observability at scale.
The Problem: Knowing What’s “Good”
One of the hardest parts of building agents is measuring how well they perform in the real world.
Offline testing relies on hand-picked examples and happy-path scenarios, missing the messy diversity of real usage. Developers manually prompt models, evaluate responses, and tune prompts by guesswork—a slow, incomplete feedback loop.
Production debugging floods developers with traces and logs but provides little guidance on which interactions actually matter. Finding failures means painstakingly reconstructing sessions and manually labeling quality issues.
You can’t score every response with an LLM-as-judge (too expensive, too slow) or manually review every trace (doesn’t scale). What you need are behavioral signals—fast, economical proxies that don’t label quality outright but dramatically shrink the search space, pointing to sessions most likely to be broken or brilliant.
What Are Behavioral Signals?
Behavioral signals are canaries in the coal mine—early, objective indicators that something may have gone wrong (or gone exceptionally well). They don’t explain why an agent failed, but they reliably signal where attention is needed.
These signals emerge naturally from the rhythm of interaction:
A user rephrasing the same request
Sharp increases in conversation length
Frustrated follow-up messages (ALL CAPS, “this doesn’t work”, excessive !!!/???)
Agent repetition / looping
Expressions of gratitude or satisfaction
Requests to speak to a human / contact support
Individually, these clues are shallow; together, they form a fingerprint of agent performance. Embedded directly into traces, they make it easy to spot friction as it happens: where users struggle, where agents loop, and where escalations occur.
Signals vs Response Quality
Behavioral signals and response quality are complementary.
Response Quality
Domain-specific correctness: did the agent do the right thing given business rules, user intent, and operational context? This often requires subject-matter experts or outcome instrumentation and is time-intensive but irreplaceable.
Behavioral Signals
Observable patterns that correlate with quality: high repair frequency, excessive turns, frustration markers, repetition, escalation, and positive feedback. Fast to compute and valuable for prioritizing which traces deserve inspection.
Used together, signals tell you where to look, and quality evaluation tells you what went wrong (or right).
How It Works
Signals are computed automatically by the gateway and emitted as OpenTelemetry trace attributes to your existing observability stack (Jaeger, Honeycomb, Grafana Tempo, etc.). No additional libraries or instrumentation required—just configure your OTEL collector endpoint.
Each conversation trace is enriched with signal attributes that you can query, filter, and visualize in your observability platform. The gateway analyzes message content (performing text normalization, Unicode handling, and pattern matching) to compute behavioral signals in real-time.
OTEL Trace Attributes
Signal data is exported as structured span attributes:
signals.quality - Overall assessment (Excellent/Good/Neutral/Poor/Severe)
signals.turn_count - Total number of turns in the conversation
signals.efficiency_score - Efficiency metric (0.0-1.0)
signals.repair.count - Number of repair attempts detected (when present)
signals.repair.ratio - Ratio of repairs to user turns (when present)
signals.frustration.count - Number of frustration indicators detected
signals.frustration.severity - Frustration level (0-3)
signals.repetition.count - Number of repetition instances detected
signals.escalation.requested - Boolean escalation flag (“true” when present)
signals.positive_feedback.count - Number of positive feedback indicators
Visual Flag Marker
When concerning signals are detected (frustration, looping, escalation, or poor/severe quality), the flag marker 🚩 is automatically appended to the span’s operation name, making problematic traces easy to spot in your trace visualizations.
Querying in Your Observability Platform
Example queries:
Find all severe interactions: signals.quality = "Severe"
Find flagged traces: search for 🚩 in span names
Find long conversations: signals.turn_count > 10
Find inefficient interactions: signals.efficiency_score < 0.5
Find high repair rates: signals.repair.ratio > 0.3
Find frustrated users: signals.frustration.severity >= 2
Find looping agents: signals.repetition.count >= 3
Find positive interactions: signals.positive_feedback.count >= 2
Find escalations: signals.escalation.requested = "true"
Core Signal Types
The signals system tracks six categories of behavioral indicators.
Turn Count & Efficiency
What it measures
Number of user–assistant exchanges.
Why it matters
Long conversations often indicate unclear intent resolution, confusion, or inefficiency. Very short conversations can correlate with crisp resolution.
Key metrics
Total turn count
Warning thresholds (concerning: >7 turns, excessive: >12 turns)
Efficiency score (0.0–1.0)
Efficiency scoring
Baseline expectation is ~5 turns (tunable). Efficiency stays at 1.0 up to the baseline, then declines with an inverse penalty as turns exceed baseline:
efficiency = 1 / (1 + 0.3 * (turns - baseline))
Follow-Up & Repair Frequency
What it measures
How often users clarify, correct, or rephrase requests. This is a user signal tracking query reformulation behavior—when users must repair or rephrase their requests because the agent didn’t understand or respond appropriately.
Why it matters
High repair frequency is a proxy for misunderstanding or intent drift. When users repeatedly rephrase the same request, it indicates the agent is failing to grasp or act on the user’s intent.
Key metrics
Repair count and ratio (repairs / user turns)
Concerning threshold: >30% repair ratio
Detected repair phrases (exact or fuzzy)
Common patterns detected
Explicit corrections: “I meant”, “correction”
Negations: “No, I…”, “that’s not”
Rephrasing: “let me rephrase”, “to clarify”
Mistake acknowledgment: “my mistake”, “I was wrong”
“Similar rephrase” heuristic based on token overlap (with stopwords downweighted)
User Frustration
What it measures
Observable frustration indicators and emotional escalation.
Why it matters
Catching frustration early enables intervention before users abandon or escalate.
Detection patterns
Complaints: “this doesn’t work”, “not helpful”, “waste of time”
Confusion: “I don’t understand”, “makes no sense”, “I’m confused”
Tone markers:
ALL CAPS (>=10 alphabetic chars and >=80% uppercase)
Excessive punctuation (>=3 exclamation marks or >=3 question marks)
Profanity: token-based (avoids substring false positives like “absolute” -> “bs”)
Severity levels
None (0): no indicators
Mild (1): 1–2 indicators
Moderate (2): 3–4 indicators
Severe (3): 5+ indicators
Repetition & Looping
What it measures
Assistant repetition / degenerative loops. This is an assistant signal tracking when the agent repeats itself, fails to follow instructions, or gets stuck in loops—indicating the agent is not making progress or adapting its responses.
Why it matters
Often indicates missing state tracking, broken tool integration, prompt issues, or the agent ignoring user corrections. High repetition means the agent is not learning from the conversation context.
Detection method
Compare assistant messages using bigram Jaccard similarity
Classify:
Exact: similarity >= 0.85
Near-duplicate: similarity >= 0.50
Looping is flagged when repetition instances exceed 2 in a session.
Severity levels
None (0): 0 instances
Mild (1): 1–2 instances
Moderate (2): 3–4 instances
Severe (3): 5+ instances
Positive Feedback
What it measures
User expressions of satisfaction, gratitude, and success.
Why it matters
Strong positive signals identify exemplar traces for prompt engineering and evaluation.
Detection patterns
Gratitude: “thank you”, “appreciate it”
Satisfaction: “that’s great”, “awesome”, “love it”
Success confirmation: “got it”, “that worked”, “perfect”
Confidence scoring
1 indicator: 0.6
2 indicators: 0.8
3+ indicators: 0.95
Escalation Requests
What it measures
Requests for human help/support or threats to quit.
Why it matters
Escalation is a strong signal that the agent failed to resolve the interaction.
Detection patterns
Human requests: “speak to a human”, “real person”, “live agent”
Support: “contact support”, “customer service”, “help desk”
Quit threats: “I’m done”, “forget it”, “I give up”
Overall Quality Assessment
Signals are aggregated into an overall interaction quality on a 5-point scale.
Excellent
Strong positive signals, efficient resolution, low friction.
Good
Mostly positive with minor clarifications; some back-and-forth but successful.
Neutral
Mixed signals; neither clearly good nor bad.
Poor
Concerning negative patterns (high friction, multiple repairs, moderate frustration). High abandonment risk.
Severe
Critical issues—escalation requested, severe frustration, severe looping, or excessive turns (>12). Requires immediate attention.
This assessment uses a scoring model that weighs positive factors (efficiency, positive feedback) against negative ones (frustration, repairs, repetition, escalation).
Sampling and Prioritization
In production, trace data is overwhelming. Signals provide a lightweight first layer of analysis to prioritize which sessions deserve review.
Workflow:
Gateway captures conversation messages and computes signals
Signal attributes are emitted to OTEL spans automatically
Your observability platform ingests and indexes the attributes
Query/filter by signal attributes to surface outliers (poor/severe and exemplars)
Review high-information traces to identify improvement opportunities
Update prompts, routing, or policies based on findings
Redeploy and monitor signal metrics to validate improvements
This creates a reinforcement loop where traces become both diagnostic data and training signal.
Trace Filtering and Telemetry
Signal attributes are automatically added to OpenTelemetry spans, making them immediately queryable in your observability platform.
Visual Filtering
When concerning signals are detected, the flag marker 🚩 (U+1F6A9) is automatically appended to the span’s operation name. This makes flagged sessions immediately visible in trace visualizations without requiring attribute filtering.
Example Span Attributes:
# Span name: "POST /v1/chat/completions gpt-4 🚩"
signals.quality = "Severe"
signals.turn_count = 15
signals.efficiency_score = 0.234
signals.repair.count = 4
signals.repair.ratio = 0.571
signals.frustration.severity = 3
signals.frustration.count = 5
signals.escalation.requested = "true"
signals.repetition.count = 4
Building Dashboards
Use signal attributes to build monitoring dashboards in Grafana, Honeycomb, Datadog, etc.:
Quality distribution: Count of traces by signals.quality
P95 turn count: 95th percentile of signals.turn_count
Average efficiency: Mean of signals.efficiency_score
High repair rate: Percentage where signals.repair.ratio > 0.3
Frustration rate: Percentage where signals.frustration.severity >= 2
Escalation rate: Percentage where signals.escalation.requested = "true"
Looping rate: Percentage where signals.repetition.count >= 3
Positive feedback rate: Percentage where signals.positive_feedback.count >= 1
Creating Alerts
Set up alerts based on signal thresholds:
Alert when severe interaction count exceeds threshold in 1-hour window
Alert on sudden spike in frustration rate (>2x baseline)
Alert when escalation rate exceeds 5% of total conversations
Alert on degraded efficiency (P95 turn count increases >50%)
Best Practices
Start simple:
Alert or page on Severe sessions (or on spikes in Severe rate)
Review Poor sessions within 24 hours
Sample Excellent sessions as exemplars
Combine multiple signals to infer failure modes:
Looping: repetition severity >= 2 + excessive turns
User giving up: frustration severity >= 2 + escalation requested
Misunderstood intent: repair ratio > 30% + excessive turns
Working well: positive feedback + high efficiency + no frustration
Limitations and Considerations
Signals don’t capture:
Task completion / real outcomes
Factual or domain correctness
Silent abandonment (user leaves without expressing frustration)
Non-English nuance (pattern libraries are English-oriented)
Mitigation strategies:
Periodically sample flagged sessions and measure false positives/negatives
Tune baselines per use case and user population
Add domain-specific phrase libraries where needed
Combine signals with non-text metrics (tool failures, disconnects, latency)
Behavioral signals complement—but do not replace—domain-specific response quality evaluation. Use signals to prioritize which traces to inspect, then apply domain expertise and outcome checks to diagnose root causes.
The flag marker in the span name provides instant visual feedback in trace UIs, while the structured attributes (signals.quality, signals.frustration.severity, etc.) enable powerful querying and aggregation in your observability platform.
See Also
../guides/observability/tracing - Distributed tracing for agent systems
../guides/observability/monitoring - Metrics and dashboards
../guides/observability/access_logging - Request/response logging
../guides/observability/observability - Complete observability guide
---
Intro to Plano
--------------
Doc: get_started/intro_to_plano
@ -2342,7 +2782,7 @@ Doc: get_started/overview
Overview
Plano is delivery infrastructure for agentic apps. A models-native proxy server and data plane designed to help you build agents faster, and deliver them reliably to production.
Plano is delivery infrastructure for agentic apps. A smart proxy server and data plane designed to help you build agents faster, and deliver them reliably to production.
Plano pulls out the rote plumbing work (the “hidden AI middleware”) and decouples you from brittle, ever‑changing framework abstractions. It centralizes what shouldn’t be bespoke in every codebase like agent routing and orchestration, rich agentic signals and traces for continuous improvement, guardrail filters for safety and moderation, and smart LLM routing APIs for UX and DX agility. Use any language or AI framework, and ship agents to production faster with Plano.
@ -3395,6 +3835,130 @@ tools.
Understanding Plano Traces
Plano creates structured traces that capture the complete flow of requests through your AI system. Each trace consists of multiple spans representing different stages of processing.
Inbound Request Handling
When a request enters Plano, it creates an inbound span (plano(inbound)) that represents the initial request reception and processing. This span captures:
HTTP request details (method, path, headers)
Request payload size
Initial validation and authentication
Orchestration & Routing
For agent systems, Plano performs intelligent routing through orchestration spans:
Agent Orchestration (plano(orchestrator)): When multiple agents are available, Plano uses an LLM to analyze the user’s intent and select the most appropriate agent. This span captures the orchestration decision-making process.
LLM Routing (plano(routing)): For direct LLM requests, Plano determines the optimal endpoint based on your routing strategy (round-robin, least-latency, cost-optimized). This span includes:
Routing strategy used
Selected upstream endpoint
Route determination time
Fallback indicators (if applicable)
Agent Processing
When requests are routed to agents, Plano creates spans for agent execution:
Agent Filter Chains (plano(filter)): If filters are configured (guardrails, context enrichment, query rewriting), each filter execution is captured in its own span, showing the transformation pipeline.
Agent Execution (plano(agent)): The main agent processing span that captures the agent’s work, including any tools invoked and intermediate reasoning steps.
Outbound LLM Calls
All LLM calls—whether from Plano’s routing layer or from agents—are traced with LLM spans (plano(llm)) that capture:
Model name and provider (e.g., gpt-4, claude-3-sonnet)
Request parameters (temperature, max_tokens, top_p)
Token usage (prompt_tokens, completion_tokens)
Streaming indicators and time-to-first-token
Response metadata
Example Span Attributes:
# LLM call span
llm.model = "gpt-4"
llm.provider = "openai"
llm.usage.prompt_tokens = 150
llm.usage.completion_tokens = 75
llm.duration_ms = 1250
llm.time_to_first_token = 320
Handoff to Upstream Services
When Plano forwards requests to upstream services (agents, APIs, or LLM providers), it creates handoff spans (plano(handoff)) that capture:
Upstream endpoint URL
Request/response sizes
HTTP status codes
Upstream response times
This creates a complete end-to-end trace showing the full request lifecycle through all system components.
Behavioral Signals in Traces
Plano automatically enriches OpenTelemetry traces with ../../concepts/signals — behavioral quality indicators computed from conversation patterns. These signals are attached as span attributes, providing immediate visibility into interaction quality.
What Signals Provide
Signals act as early warning indicators embedded in your traces:
Quality Assessment: Overall interaction quality (Excellent/Good/Neutral/Poor/Severe)
Efficiency Metrics: Turn count, efficiency scores, repair frequency
User Sentiment: Frustration indicators, positive feedback, escalation requests
Agent Behavior: Repetition detection, looping patterns
Visual Flag Markers
When concerning signals are detected (frustration, looping, escalation, or poor/severe quality), Plano automatically appends a flag marker 🚩 to the span’s operation name. This makes problematic traces immediately visible in your tracing UI without requiring additional queries.
Example Span with Signals:
# Span name: "POST /v1/chat/completions gpt-4 🚩"
# Standard LLM attributes:
llm.model = "gpt-4"
llm.usage.total_tokens = 225
# Behavioral signal attributes:
signals.quality = "Severe"
signals.turn_count = 15
signals.efficiency_score = 0.234
signals.frustration.severity = 3
signals.escalation.requested = "true"
Querying Signal Data
In your observability platform (Jaeger, Grafana Tempo, Datadog, etc.), filter traces by signal attributes:
Find severe interactions: signals.quality = "Severe"
Find frustrated users: signals.frustration.severity >= 2
Find inefficient flows: signals.efficiency_score < 0.5
Find escalations: signals.escalation.requested = "true"
For complete details on all available signals, detection methods, and best practices, see the ../../concepts/signals guide.
Benefits of Using Traceparent Headers
Standardization: The W3C Trace Context standard ensures compatibility across ecosystem tools, allowing
@ -4826,7 +5390,7 @@ Welcome to Plano!
Plano is delivery infrastructure for agentic apps. A models-native proxy server and data plane designed to help you build agents faster, and deliver them reliably to production.
Plano is delivery infrastructure for agentic apps. A smart proxy server and data plane designed to help you build agents faster, and deliver them reliably to production.
Plano pulls out the rote plumbing work (aka “hidden AI middleware”) and decouples you from brittle, ever‑changing framework abstractions. It centralizes what shouldn’t be bespoke in every codebase like agent routing and orchestration, rich agentic signals and traces for continuous improvement, guardrail filters for safety and moderation, and smart LLM routing APIs for UX and DX agility. Use any language or AI framework, and ship agents to production faster with Plano.

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</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="concepts/prompt_target.html">Prompt Target</a></li>
<li class="toctree-l1"><a class="reference internal" href="concepts/signals.html">Signals™</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Guides</span></p>
<ul>
@ -150,7 +151,7 @@
<h1>Welcome to Plano!<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() =&gt; $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#welcome-to-plano"><svg height="1em" viewbox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76 0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76 0 5-2.24 5-5s-2.24-5-5-5z"></path></svg></a></h1>
<a class="reference internal image-reference" href="_images/PlanoTagline.svg"><img alt="_images/PlanoTagline.svg" class="align-center" src="_images/PlanoTagline.svg" style="width: 100%;"/>
</a>
<p><a class="reference external" href="https://github.com/katanemo/plano" rel="nofollow noopener">Plano<svg fill="currentColor" height="1em" stroke="none" viewbox="0 96 960 960" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M188 868q-11-11-11-28t11-28l436-436H400q-17 0-28.5-11.5T360 336q0-17 11.5-28.5T400 296h320q17 0 28.5 11.5T760 336v320q0 17-11.5 28.5T720 696q-17 0-28.5-11.5T680 656V432L244 868q-11 11-28 11t-28-11Z"></path></svg></a> is delivery infrastructure for agentic apps. A models-native proxy server and data plane designed to help you build agents faster, and deliver them reliably to production.</p>
<p><a class="reference external" href="https://github.com/katanemo/plano" rel="nofollow noopener">Plano<svg fill="currentColor" height="1em" stroke="none" viewbox="0 96 960 960" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M188 868q-11-11-11-28t11-28l436-436H400q-17 0-28.5-11.5T360 336q0-17 11.5-28.5T400 296h320q17 0 28.5 11.5T760 336v320q0 17-11.5 28.5T720 696q-17 0-28.5-11.5T680 656V432L244 868q-11 11-28 11t-28-11Z"></path></svg></a> is delivery infrastructure for agentic apps. A smart proxy server and data plane designed to help you build agents faster, and deliver them reliably to production.</p>
<p>Plano pulls out the rote plumbing work (aka “hidden AI middleware”) and decouples you from brittle, ever‑changing framework abstractions. It centralizes what shouldn’t be bespoke in every codebase like agent routing and orchestration, rich agentic signals and traces for continuous improvement, guardrail filters for safety and moderation, and smart LLM routing APIs for UX and DX agility. Use any language or AI framework, and ship agents to production faster with Plano.</p>
<p>Built by contributors to the widely adopted <a class="reference external" href="https://www.envoyproxy.io/" rel="nofollow noopener">Envoy Proxy<svg fill="currentColor" height="1em" stroke="none" viewbox="0 96 960 960" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M188 868q-11-11-11-28t11-28l436-436H400q-17 0-28.5-11.5T360 336q0-17 11.5-28.5T400 296h320q17 0 28.5 11.5T760 336v320q0 17-11.5 28.5T720 696q-17 0-28.5-11.5T680 656V432L244 868q-11 11-28 11t-28-11Z"></path></svg></a>, Plano <strong>helps developers</strong> focus more on the core product logic of agents, <strong>product teams</strong> accelerate feedback loops for reinforcement learning, and <strong>engineering teams</strong> standardize policies and access controls across every agent and LLM for safer, more reliable scaling.</p>
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<li class="toctree-l1"><a class="reference internal" href="concepts/signals.html">Signals™</a></li>
</ul>
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<li class="toctree-l1"><a class="reference internal" href="../concepts/signals.html">Signals™</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../../concepts/signals.html">Signals™</a></li>
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