Learning & Evolution
Learn from experience.
Keep control.
How your agentic system can get better at its work: learn from outcomes, improve the harness around the model, and release only changes that pass independent checks. We call this governed adaptation.
The neuroplasticity connection. This is the practical interpretation of our neuroplasticity-based research: use experience to guide change while protecting what must stay reliable. It is a design metaphor, not a claim that software learns like a brain.
Explore the research & metaphor ↗01 / What gets better?
Illustrative examples · not measured results
Same request. Better evidence.
A customer asks to change the delivery address on an order that is already paid for—the same example used in Journey.
Before / the problem
The relevant policy arrives too late.
A general shipping FAQ fills the context. The address-change policy is ranked too low to help the agent answer.
After / intended, if validated
The right policy reaches the agent first.
A revised retrieval rule prioritizes the applicable address-change policy, with its source, before the agent decides what to do.
What stays protected. The model can stay the same. Source permissions, customer identity checks and approval requirements still apply.
The Retrieval harness · 15 ↗02 / How learning becomes a safe change
Feedback is the beginning.
Not permission to change.
Review completed work, test a candidate, then decide whether to release it. This improvement loop sits outside the live task timeline. Select a step to follow the example.
01 / Observability + task owner
Notice a pattern
Review completed address-change tasks. In this example, the applicable policy exists, but a generic FAQ repeatedly displaces it from the supplied context.
Review recorded outcomes, user feedback and failures. This review is separate from the live task; it does not change the system by itself.
If the evidence fails or a release regresses
Inspect the technical change envelope
03 / The limits behind the example
Define what may change.
Protect what must not.
This change envelope records the permissions, evidence and recovery conditions for “Improve retrieval.” It is the technical contract behind the learning loop.
- What may change
- Reranking configuration and context-selection policy on a versioned corpus.
- What stays protected
- Source entitlements, tenant boundaries, citation provenance and safety policy.
- Tests required
- Held-out grounding tests, retained-task regressions, freshness and permission-denial cases.
- Who may approve
- Governance approves the exact candidate after independent Evaluation evidence and Security review.
- Resource limits
- A declared evaluation budget, bounded search space and limited canary traffic.
- When to stop and recover
- Withdraw on grounding regression, provenance loss or any denied-source disclosure; restore the last approved configuration.
This is a reference design, not a claim that Planeon runs self-improving production agents or that these examples passed an assessment. A change envelope does not establish regulatory compliance. Adaptation permission, AML maturity, implementation readiness and research maturity remain distinct. Evolution crosses the existing planes; it is not a seventeenth harness.