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A self-learning loop is an agent that gets better the more it’s used — not by retraining the model, but by capturing what it learns and making that learning available to its future self (and to every other agent on the same knowledge base). Done well, it’s compounding. Done naively, it’s how agents poison themselves.

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The naive version (and why it drifts)

The tempting approach is to append learnings to a prompt or a running markdown file: “remember that checkout uses the new payment provider.” It works for a day, then:
  • The file grows unbounded and starts poisoning context.
  • New learnings contradict old ones with no resolution.
  • There’s no provenance — you can’t tell what the agent taught itself versus what a human verified.
Unstructured write-back turns your knowledge base into exactly the mess this whole guide is about avoiding.

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The structured version

Close the loop through the same structured, validated store the agent reads from — so learning is a first-class write, not a growing text blob:
1

Capture learning as a structured write

When the agent discovers something durable, it writes an entity (or updates one) — validated against the schema, addressed by id, carrying provenance. Not a line appended to a doc. → put_entity
2

Update in place, don't accumulate

A new fact about service:checkout updates that record. The knowledge base stays correct instead of growing a pile of contradictory notes. → Conflicts & staleness
3

Trigger it automatically

Wire the write-back to fire at the end of a session — e.g. a Stop hook that records what was learned via put_entity. That’s the “continuously improving” part: it happens without anyone remembering to do it. → populating entities
4

Keep human-verified and agent-asserted distinguishable

Provenance (asserted_by, confidence) marks what the agent taught itself versus what a human confirmed — so you can trust, review, or decay agent-written facts differently. → Provenance

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Why the store matters more than the trigger

The trigger (a hook, a cron, an end-of-task step) is easy. What makes the loop safe is that it writes into a store that validates, addresses, and attributes every fact — so more usage makes the knowledge base sharper, not noisier. A learning loop over unstructured files amplifies drift; a learning loop over a structured brain compounds quality.
Because every agent on the same brain reads the same store, one agent’s learning becomes every agent’s context. The loop isn’t just self-improving — it’s fleet-improving. → connected knowledge layer

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Checklist

  • Learnings are written as validated, addressed entities — not appended text.
  • New facts update existing records instead of accumulating.
  • Write-back is triggered automatically (hook / end-of-session).
  • Agent-asserted vs human-verified facts are distinguishable via provenance.
  • Agent-written facts are subject to review and/or decay.

Back to the guide

See how every piece fits into building an autonomous agent.