cao-learning

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Report task outcomes and distill lessons so the team improves across runs — report_outcome after each unit of work, retrospector handoffs at natural boundaries, and applying injected lessons. Use in workflows that run repeatedly over similar work items. Requires memory.learning_enabled; degrade silently when the tools report disabled.

AI & Automation 1,089 stars 227 forks Updated today Apache-2.0

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# CAO Self-Learning CAO workflows can improve as they repeat: outcomes you report feed a retrospector agent that distills durable lessons into memory, and those lessons reach future sessions automatically. Your job depends on your role. All of this is opt-in infrastructure. **If `report_outcome` or a memory tool returns `disabled: true`, skip it silently and continue your task** — learning is off for this run (often deliberately, e.g. a control run) and that is expected, not an error. ## If you are a SUPERVISOR ### Report an outcome after each meaningful unit of work One `report_outcome` call per completed step, delegated task, or work item — after validation/review, not before: ``` report_outcome( task_label="convert package CustomerETL (iteration 2)", success=false, workflow_name="ssis-migration", agent_profile="transformer", # who did the work (defaults to you) score=40, # optional 0-100 metric if you have one friction_notes="Lookup with partial cache emitted an invalid join; " "improver patched the cache-mode mapping." ) ``` Rules for `friction_notes`: - 1–3 sentences, **conclusions only** — the root cause, not the story. - NEVER paste transcripts, logs, stack traces, file contents, or secrets. - Empty string on a clean pass is fine; the success flag already carries signal. Report failures faithfully — failed iterations are the most valuable learning signal. Do not skip reporting bec...

Details

Author
awslabs
Repository
awslabs/cli-agent-orchestrator
Created
1 years ago
Last Updated
today
Language
Python
License
Apache-2.0

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