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Three layers of Pydantic models under app/automations/schemas/, one
file per concern (SRP), matching the envelope in
automation-design-plan.md §5.
definition/ — the editable envelope persisted in
automations.definition:
- envelope.py AutomationDefinition (top-level shape)
- plan_step.py PlanStep (one step in the sequential plan)
- inputs.py InputsBlock (the inputs JSON Schema wrapper)
- execution.py ExecutionBlock (timeouts, retries, concurrency,
budget cap, on_failure plan)
- metadata.py MetadataBlock (tags + created_from_nl + extras)
- trigger_spec.py TriggerSpec (one entry in triggers[])
triggers/ — per-trigger config schemas, dispatched by registry on the
TriggerSpec.type discriminator:
- schedule.py ScheduleTriggerConfig(cron, timezone)
- manual.py ManualTriggerConfig() — empty in v1
actions/ — per-action config schemas, dispatched by registry on the
PlanStep.action discriminator:
- agent_task.py AgentTaskActionConfig(prompt, tools, model,
output_schema)
Design properties verified by an inline smoke test:
- The §5 worked example round-trips through model_validate_json /
model_dump_json byte-for-byte (InputsBlock uses
serialize_by_alias so the JSON key stays "schema" not
"schema_").
- Envelope rejects unknown top-level keys (extra="forbid").
- MetadataBlock tolerates unknown keys (extra="allow").
- ExecutionBlock defaults apply when the block is omitted.
- retry_backoff and concurrency are typed as Literal — bogus
values rejected at validation time.
- Per-type configs enforce their required fields (cron + timezone
on schedule; non-empty prompt on agent_task).
The envelope keeps trigger and action configs as untyped dicts on
purpose — per-type validation is a registry-driven dispatch (commit
10), keeping the envelope free of every-type-knows-every-type
coupling.
66 lines
2.3 KiB
Python
66 lines
2.3 KiB
Python
"""``AgentTaskActionConfig`` — config for the ``agent_task`` action type."""
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from __future__ import annotations
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from typing import Any
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from pydantic import BaseModel, ConfigDict, Field
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class AgentTaskActionConfig(BaseModel):
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"""Config for an ``agent_task`` plan step.
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Validated against ``PlanStep.config`` whenever the step's
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``action`` is ``agent_task``. The step instructs the LangGraph
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Deep Agent runtime to:
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1. Receive ``prompt`` (with all preceding-step outputs and inputs
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already rendered by the template engine).
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2. Run the agent with access to *exactly* the capabilities named
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in ``tools`` — nothing else from the registry is visible to
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this agent invocation.
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3. Return a JSON object matching ``output_schema`` (recommended;
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the executor validates and re-prompts on mismatch).
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``output_schema`` is the design's "dynamic output contract" —
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instead of locking the output shape on the ActionDefinition (as
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tight actions do), the user declares the shape they want for this
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specific step, and the agent has to match it.
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"""
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model_config = ConfigDict(extra="forbid")
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prompt: str = Field(
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...,
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description=(
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"The task prompt rendered through the Jinja sandbox. May "
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"reference automation inputs and prior-step outputs."
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),
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min_length=1,
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)
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tools: list[str] = Field(
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default_factory=list,
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description=(
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"Allowlist of capability IDs the agent may call (e.g., "
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"'search_space.query'). Empty list = no tool access; the "
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"agent must answer from the prompt alone."
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),
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)
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model: str | None = Field(
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default=None,
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description=(
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"Optional LiteLLM model identifier (e.g., "
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"'anthropic/claude-sonnet-4-7'). Omitted means the "
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"automation falls back to the search space's default "
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"agent_llm_id."
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),
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)
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output_schema: dict[str, Any] | None = Field(
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default=None,
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description=(
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"Optional JSON Schema declaring the shape the agent must "
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"return. Strongly recommended; the editor warns when "
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"missing. Validated by the executor before binding to "
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"``output_as``."
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),
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)
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