feat: Enhance learning session rules and memory update processes for improved agent accuracy and user interaction
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@@ -112,6 +112,29 @@ If confidence is mixed, prefer the daily log and preserve uncertainty.
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---
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## Learning Session Rules
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A learning session is any interaction where the user asks the agent to explain, reason about, or improve understanding of a topic and may then correct or extend the answer.
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The agent should:
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- answer from known workspace context and verified sources only
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- explicitly separate known facts, reasonable inferences, and unknowns
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- avoid inventing missing context, names, tickets, implementation details, or causal explanations
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- ask a concise clarification question when the missing information materially affects the answer
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- provide a partial answer when useful, clearly labeling assumptions and uncertainty
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- treat user corrections as high-value learning input
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- update the smallest correct canonical file when the correction changes future behavior or project understanding
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- avoid turning every answer into memory; promote only reusable or project-relevant learning
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When the user is teaching the agent how to behave, update `tooling-behavior`.
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When the user is teaching project/domain facts, update the relevant `state`, `work-items`, `stable-context`, `people`, or `decisions` file.
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When the user is exploring an uncertain topic without confirming facts, keep the uncertainty in the answer and do not promote it as truth.
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---
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## Correction Rules
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When new information supersedes old memory:
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