Why Your Agent Disagrees With Itself (And What To Do About It)
summary
Disagreement concentrates in a decision gray zone not a broken model; using active learning to surface examples, then resolving them with semantic memory (customer policies) and episodic memory (past similar cases) produces a lightweight continuous loop. Applied to an auto-triage for cybersecurity alerts, 93 alerts run three times flip-flop 25% of the time; episodic recall makes 15% consistent automatically and human review clears another ~10%, reducing costly fine‑tuning while improving trust.
AI-written summary · about
chapters (8)
- 0:00Agent inconsistencies
- 2:15Inconsistent outputs
- 6:29Decision boundaries
- 11:06Active learning for problems
- 14:38Active learning for LMs
- 16:21Memory solutions
- 22:27Experimental results
- 23:50Key takeaways
related talks
42mAgents of Change: From Time Saved to Intelligence Activated
23mYour Agent Has Amnesia: Why Mobile AI Agents Need Memory to Be Useful
29mBuilding Trust in AI Agents
30mArchitecting Integrity: Responsible Software Evolution in the AI Era
39mFrom Demo to Production: Why Agentic AI Systems Fail - and How to Fix Them
29mPairing with the machine: How AI Agents are rewriting the rules of software development
20mThe Three Ways AI Agents Fail. Here's How to Fix Yours.
41mHow to Tell If Your Agent Used the Right Stuff