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893ad87268
* updates to the twitter prebuilt cards * update the default fallback model from gpt-4o to gpt-4.1
141 lines
3.9 KiB
Text
141 lines
3.9 KiB
Text
---
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title: "Agents"
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description: "Learn about creating and configuring individual agents within your multi-agent system"
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icon: "robot"
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---
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## Overview
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Agents are the core building blocks of Rowboat's multi-agent system. Each agent carries out a specific part of a conversation, handles tasks via tools, and can collaborate with other agents to orchestrate complex workflows.
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They are powered by LLMs and can:
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- Respond to user input
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- Trigger tools or APIs
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- Pass control to other agents using @mentions
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- Fetch or process internal data
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- Execute RAG (Retrieval-Augmented Generation) queries
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- Participate in sequential pipeline workflows
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---
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## Agent Types
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Rowboat supports several types of agents, each designed for specific use cases:
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| Name | Purpose | Characteristics |
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|------|---------|-----------------|
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| **Conversational Agents** (`conversation`) | Primary user-facing agents that interact directly with users and orchestrate workflows. | • Can respond to users and orchestrate workflows<br />• Typically serve as the start agent (Hub Agent)|
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| **Task Agents** (`internal`) | Specialized agents that perform specific tasks without direct user interaction. | • Focused on specific functions<br />• Return results to parent agents|
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| **Pipeline Agents** (`pipeline`) | Sequential workflow execution agents that process data in a chain. | • Execute in sequence within a pipeline<br />• Cannot transfer to other agents directly|
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---
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## Agent Configuration
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Agents are configured through two main tabs in the Rowboat Studio interface:
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### **Instructions Tab**
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#### Description
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A clear description of the agent's role and responsibilities
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#### Instructions
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Instructions are the backbone of the agent's behavior. Use the Copilot's structured format for consistency:
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**Recommended Structure:**
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```
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## 🧑💼 Role:
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[Clear description of the agent's role]
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## ⚙️ Steps to Follow:
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1. [Step 1]
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2. [Step 2]
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3. [Step 3]
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## 🎯 Scope:
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✅ In Scope:
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- [What the agent should handle]
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❌ Out of Scope:
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- [What the agent should NOT handle]
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## 📋 Guidelines:
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✔️ Dos:
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- [Positive behaviors]
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🚫 Don'ts:
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- [Negative behaviors]
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```
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#### Examples
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These help agents behave correctly in specific situations. Each example can include:
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- A sample user message
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- The expected agent response
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- Any tool calls (if applicable)
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### **Configurations Tab**
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#### Name
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Name of the agent
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#### Behaviour
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- **Agent Type**: Choose from `conversation`, `internal`, or `pipeline`
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- **Model**: Select the LLM model (GPT-4.1, GPT-4o, google/gemini-2.5-flash, etc.)
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#### RAG
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- **Add Source**: Connect data sources to enable RAG capabilities for the agent
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---
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## Creating Your Initial Set of Agents
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Let Copilot bootstrap your agent graph.
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### Instruct Copilot
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Start by telling Copilot what your assistant is meant to do — it'll generate an initial set of agents with best-practice instructions, role definitions, and connected agents.
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<Frame>
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<img src="/docs/img/create-agents-delivery.png" className="w-full max-w-[400px] rounded-xl" alt="Creating agents with Copilot" />
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</Frame>
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### Inspect the Output
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After applying the suggested agents, take a close look at each one's:
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- **Instructions**: Define how the agent behaves
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- **Examples**: Guide agent responses and tool use
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<Frame>
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<img src="/docs/img/agent-instruction.png" alt="Inspect agent instructions" />
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</Frame>
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---
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## Updating Agent Behavior
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There are three ways to update an agent:
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### 1. With Copilot
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Copilot understands the current chat context and can help rewrite or improve an agent's behavior based on how it performed.
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<Frame>
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<img src="/docs/img/update-agent-copilot.png" className="w-full max-w-[400px] rounded-xl" alt="Update agent using Copilot" />
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</Frame>
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### 2. Manual Edits
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You can always manually edit the agent's instructions.
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<Frame>
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<img src="/docs/img/update-agent-manual.png" alt="Manually edit agent" />
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</Frame>
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---
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