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docs: add evolution config controls (#2852)
* docs: add evolution config controls * docs: address evolution config review
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@@ -7,6 +7,7 @@ Internal architecture notes for major runtime mechanisms and subsystem design.
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- [Session System](session-system.md): session scope allocation, JSONL persistence, alias compatibility, and migration. ([ZH](session-system.zh.md))
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- [Routing System](routing-system.md): agent dispatch, session policy selection, and light/heavy model routing. ([ZH](routing-system.zh.md))
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- [Runtime Events](runtime-events.md): runtime event envelope, centralized event logging, filters, and examples. ([ZH](runtime-events.zh.md))
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- [Agent Self-Evolution](agent-self-evolution.md): learning records, draft generation, application modes, and state layout.
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- [Hook System Guide](hooks/README.md): current hook architecture and protocol details.
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- [Agent Refactor](agent-refactor/README.md): notes and checkpoints for the agent refactor work.
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@@ -0,0 +1,47 @@
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# Agent Self-Evolution
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Agent self-evolution lets PicoClaw learn from completed turns and turn repeated successful behavior into skill improvements. The runtime is controlled by the top-level `evolution` config block.
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## Flow
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The hot path runs at the end of an agent turn. When `evolution.enabled` is true, it records a learning record with the turn summary, success state, used skills, tool executions, and session/workspace metadata. Heartbeat turns are skipped.
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The cold path groups related task records, checks the configured success threshold, and prepares skill drafts for patterns that have enough evidence. Drafts can target new skills or append/replace/merge existing workspace skills.
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The apply path validates generated `SKILL.md` content before writing. Invalid drafts are rejected before a skill directory or file is created.
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## Safety Considerations
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Evolution creates a persistent feedback loop: user input can become a task record, task records can be clustered into an LLM-generated draft, and an accepted draft can become `SKILL.md` content that is loaded into future agent prompts. Treat generated skill content as prompt-sensitive material, especially in `apply` mode.
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The current local scanner is a narrow guardrail, not a complete safety boundary. It rejects structurally invalid drafts and a small set of obvious secret-like substrings, but it does not reliably detect prompt injection, unsafe instructions, or every form of sensitive data. Use `observe` or `draft` when human review is required before skill changes reach disk.
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In `apply` mode, accepted drafts can update workspace skills automatically. Existing skills are backed up before replacement, but recovery is manual: an operator must restore the desired backup if an applied skill should be rolled back.
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## Modes
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| Mode | Behavior |
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|------|----------|
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| `observe` | Record learning data only. No cold-path draft generation runs automatically. |
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| `draft` | Record learning data and generate candidate skill drafts when the cold path runs. |
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| `apply` | Generate drafts and allow accepted drafts to update workspace skills. |
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When `evolution.enabled` is false, `mode` is treated as disabled at runtime.
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## Cold Path Trigger
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`cold_path_trigger` only matters in `draft` and `apply` modes.
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| Trigger | Behavior |
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|---------|----------|
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| `after_turn` | Run the cold path after eligible turns. |
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| `scheduled` | Run the cold path at configured `cold_path_times`. |
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| `manual` | Do not run automatically. There is no user-facing Web/API/CLI trigger yet; code can still invoke `Runtime.RunColdPathOnce`. |
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`cold_path_times` uses `HH:MM` strings and is ignored unless the trigger is `scheduled`.
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## State
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By default, evolution state is stored under the workspace. `state_dir` can redirect that state to another directory. The state includes learning records, clustered pattern records, drafts, and skill profiles.
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For user-facing configuration fields, see the [Configuration Guide](../guides/configuration.md#agent-self-evolution).
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