Memory Audit Tracker for AI Agents

agent_name: ResearchBot-v2 memory_before: {"user_preference": "prefers bullet points", "last_topic": "climate change", "trust_level": "low"} memory_after: {"user_preference": "prefers bullet points", "last_topic": "renewable energy policy", "trust_level": "medium", "user_expertise": "graduate researcher"}

## Memory Audit Report — ResearchBot-v2 **Summary:** 3 diffs detected — 2 SUBSTANTIVE (avg impact 3.5), 1 EXPANSION. No conflicts or retractions. | Key | Classification | Impact | Delta Summary | |---|---|---|---| | last_topic | SUBSTANTIVE | 3 | Topic shifted from broad climate change to specific policy sub-domain | | trust_level | SUBSTANTIVE | 4 | User trust upgraded from low to medium, affecting response depth and citations | | user_expertise | EXPANSION | 3 | New field added identifying user as graduate researcher, no prior contradiction | **Knowledge Drift Analysis:** ResearchBot-v2 has meaningfully updated its model of the user. It now treats the user as a domain expert (graduate researcher) and has elevated trust, which should unlock more technical, citation-heavy responses. The topic shift from 'climate change' to 'renewable energy policy' suggests the session narrowed scope — the agent should no longer volunteer broad climate overviews unprompted. **Flags:** None. **Audit Metadata:** Agent: ResearchBot-v2 | Context: after user session | Total diffs: 3 | Substantive: 2 | Expansions: 1 | Conflicts: 0