Oct 08, 2026

Giving AI Agents Memory That Learns — Jake Broekhuizen, LangChain

Jake Broekhuizen of LangChain Labs argues that observability alone does not make agents improve. Using a financial services agent whose tone drifted from advisory to pushy, he shows how traces only become useful memory when a filtered lesson is written back as durable context the agent reads on its next run. The talk covers semantic, episodic, and procedural memory, the working versus long-term memory distinction, the read-write cycle, and how LangSmith and Context Hub support capture, analysis, and updates, along with lessons about keeping only signal, caching, and human review of procedural changes.

Jul 20, 2026

Why Your Agent Disagrees With Itself (And What To Do About It) - Diane Lin, Datadog

This session addresses the critical problem of AI agent inconsistency, particularly in LLMs, which leads to "flip-flops" in high-stakes domains like cybersecurity. It argues that this isn't a model failure but a signal of ambiguity in the "gray zone" near decision boundaries. The proposed solution involves using active learning to identify these ambiguous cases, followed by augmenting agents with semantic memory (explicit policies) and episodic memory (past similar cases) to clarify decisions, improve consistency, and adapt to customer-specific preferences without relying solely on expensive fine-tuning.