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.

Oct 08, 2026

Why Bigger Context Windows Won't Save Your Agent — Elizabeth Fuentes Leone, AWS

Elizabeth Fuentes Leone, developer advocate at AWS, explains why agents break when tools return huge outputs, why bigger context windows do not fix the problem because models lose the middle of long contexts, and how four context engineering strategies (externalize, select, compress, isolate) can be implemented with the open-source Strands Agents framework.

Oct 07, 2026

The Software Factory: From Bug Report to Production Code — Davis Palmie, Factory

Davis Palmie of Factory explains the software factory, a system of agents that turns bug reports and user feedback into production code. He covers leadership concerns about cost, model choice, engineering roles, and org structure, argues that coding was never the bottleneck, and lays out principles for a factory: model agnosticism, sovereign deployment, and SDLC-wide integration. He also describes eight pillars of agent readiness, starting with narrow use cases like incident triage, and closes with why outcome metrics should replace token counts.

Oct 07, 2026

Why AI Agents Should Have Their Own Sandbox — Philipp Schmid, Google DeepMind

Philipp Schmid of Google DeepMind introduces the Gemini Interactions API, a unified interface for models and agents with server-side state and long-running jobs. He then shows managed cloud sandboxes for Antigravity agents that persist, can be shared, and automatically pick up skills and AGENTS.md files, closing with a live AI talk radio demo built from a single prompt.

Oct 06, 2026

The 6 Pillars of an Agentic Harness for Production — Varun Krovvidi, Resolve AI

Varun Krovvidi of Resolve AI explains why AI excels at code generation but struggles with the complex, multi-domain problem of running and fixing software in production. He outlines five common failure modes for AI agents in production (anchoring bias, context issues, coherent vs. causal answers, guardrails, lack of learning) and introduces Resolve AI's six-pillar architecture: model orchestration, context engineering, causal reasoning, governed actions, learning systems, and multi-level evaluations. A demo illustrates how Resolve AI diagnoses a Grafana alert to its root cause, ruling out coincidental events and maintaining its causal reasoning.

Oct 06, 2026

Move Fast and Don't Break Things: Scaling Databases for the AI Era — PlanetScale

Ben Dicken of PlanetScale argues that teams can move fast without breaking production, combining isolation, redundancy, sharding, back pressure, and decoupling with AI agent workflows such as VSchema-driven sharding, Traffic Control budgets, schema branching, and one-click reverts. His core claim is that good developer experience produces good primitives for AI.