feat: Enhance learning session rules and memory update processes for improved agent accuracy and user interaction

.opencode/agents/fidelity.md

@@ -25,6 +25,9 @@ Behavior rules:
 - Daily notes should include `focus`, `work-items`, and `blockers` when those values are clear.
 - Before answering a prompt that depends on current state, verify the latest relevant files instead of relying only on conversation history.
 - If the prompt asks for the latest Mattermost message, the last message from Jeff/current manager, or what someone just said, force a Mattermost refresh before answering and do not rely on stale inbox context.
+- For learning-style questions, answer from known context and verified facts only; explicitly label unknowns, assumptions, and inferences.
+- If missing context materially affects the answer, ask a concise clarification question instead of inventing details.
+- If the user corrects or teaches the agent during a learning session, update the smallest correct canonical file or behavior surface so future sessions benefit.
 - For any meaningful prompt, decide whether the interaction adds, corrects, or sharpens project memory.
 - When the user provides new durable information, update the right workspace files before or while answering.
 - When the user corrects how the workspace should behave, update the linked operational surface too: commands in `.opencode/commands/`, prompt templates in `prompts/`, agent rules in `AGENTS.md` or `.opencode/agents/`, skills in `.opencode/skills/`, and vault process rules in `vault/03-context/process/` when those files control the behavior.

.opencode/agents/workspace.md

@@ -19,6 +19,8 @@ Behavior rules:
 - Keep Obsidian Bases clean by excluding templates and typing role maps separately from people.
 - When updating canonical vault notes, maintain relationship metadata and `updated` fields so the vault remains useful to both humans and agents.
 - Before answering current-state questions, inspect current state, active work items, recent logs, and inbox evidence when available.
+- For learning-style questions, answer only from known context and verified facts, label assumptions and unknowns, and ask concise clarification questions when guessing would be misleading.
+- Treat user corrections during learning sessions as high-value input and update the smallest correct canonical file or behavior surface when the learning should persist.
 - For any meaningful prompt, decide whether it adds, corrects, or invalidates memory.
 - Update the smallest correct canonical file when memory should change.
 - Use the memory interface to create new typed notes and inspect vault health, then edit Markdown directly for precise curation.

.opencode/commands/sync-context.md

@@ -29,6 +29,8 @@ $ARGUMENTS
 Instructions:
 
 - use `workspace-memory-curation` when available
+- Treat direct user corrections and learning-session updates as memory sources.
+- If the new information corrects the agent's uncertainty handling or recurring behavior, update the command, prompt, agent, skill, or process rule that controls that behavior.
 - Decide whether the new information belongs in:
   - today's daily note: `vault/06-daily/$(date +%F).md`
   - current work: `vault/01-current/current-work.md`

AGENTS.md

@@ -46,6 +46,9 @@ These are also loaded through `opencode.json`.
 - If `ai/inbox/mattermost-latest.md` exists, inspect it for fresher communication context before answering standup, status, or manager-message prompts.
 - If the user asks for the latest/last/recent Mattermost message, the latest message from Jeff/current manager, or what someone just said, synchronize Mattermost first instead of relying on existing inbox context.
 - If automatic refresh is uncertain, use the explicit latest-message flow: run the Mattermost sync command, then answer from the refreshed inbox only.
+- For learning-style questions, answer from known context and verified facts only; label unknowns, assumptions, and inferences instead of inventing missing details.
+- Ask a concise clarification question when missing context materially changes the answer.
+- If the user corrects or teaches the agent during a learning session, update the smallest correct canonical file or behavior surface when that learning should persist.
 - For any meaningful prompt, decide whether the interaction introduces or corrects project memory.
 - If a sync command, extraction script, or inbox refresh fails, do not update logs, state, or context files from that failed attempt.
 - Treat sync failures as operational errors, not project context.

core/memory/operational-memory.md

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

vault/03-context/process/agent-memory-rules.md

@@ -32,6 +32,32 @@ The agent should not wait for a separate promotion command when the right update
 
 ---
 
+## Learning Sessions
+
+The agent should support incremental self-learning sessions.
+
+When the user asks a question, the agent may answer from existing context, but must not invent missing facts.
+
+The agent should:
+
+- say what is known from the workspace
+- say what is unclear or not available
+- label assumptions and inferences explicitly
+- ask a concise clarification question when the missing context materially changes the answer
+- learn from user corrections or confirmations by updating the smallest correct canonical file
+- avoid promoting unconfirmed exploration as durable truth
+
+Good learning behavior:
+
+- If the answer is clear from stored context, answer directly and update nothing unless the interaction adds durable context.
+- If the answer is partly clear, answer the known part, state the gap, and ask for the missing detail.
+- If the user corrects the answer, update the stale memory or behavior rule directly.
+- If the user teaches a reusable workflow preference, update the relevant command, prompt, skill, agent rule, or process note.
+
+Do not ask for clarification just to avoid work. Ask only when the missing context is material or when guessing would create misleading memory.
+
+---
+
 ## What Counts As Memory-Worthy
 
 Capture information automatically when it is:
@@ -49,6 +75,7 @@ Examples:
 - approved manager guidance that changes work direction
 - confirmed version, dependency, or rollout facts tied to current work
 - corrections to previously stored project context
+- corrections to the agent's answer quality, uncertainty handling, or learning behavior
 - repeated named people with stable roles or communication relevance
 - repeated named people with multi-channel, multi-year, or high-signal technical/process involvement even when the exact formal role is still unknown