Aug 11, 2026

Evolution of agentic surfaces — Gagan Bhat & Isabella Kai He, Anthropic

Anthropic's Gagan Bhat and Isabella Kai He discuss how agent harnesses must evolve rapidly to keep pace with fast-improving LLMs. They introduce Claude Managed Agents, an architecture that decouples the agent's 'brain' (reasoning) from its 'hands' (tool execution) to address issues like stale assumptions, latency, and reliability. This approach enables dynamic adaptation, secure tool execution, and features like 'dreaming' for self-improving agents and 'outcomes' for goal-oriented task completion, ultimately aiming to close the gap between model capabilities and product offerings.

Aug 09, 2026

Always-on agents run production without the on-call tax — Justin Smith, Resolve AI

Justin Smith from Resolve AI discusses how AI agents address the increasing operational burden on engineers, highlighting that 70% of an engineer's time is spent running code. He introduces Resolve AI's background agents, which autonomously monitor deployments, perform health checks, generate reports, and answer engineering questions by leveraging deep production context and a self-learning knowledge system, effectively reducing the "on-call tax" and managing system complexity.

Jul 25, 2026

From Agent Traces to Agent Simulations — Rustem Feyzkhanov, Snorkel AI

Rustem Feyzkhanov discusses the critical need for companies to build private, production-aligned benchmarks for AI agents. He explains how to turn agent traces into repeatable simulations, why public benchmarks are insufficient, and how a CI pipeline for agents, integrating observability and experimentation, can ensure reliable evaluation, continuous improvement, and effective release management, moving beyond simple pass rates to measure cost, latency, and policy adherence.

Jul 24, 2026

How Two French Engineers In New York Built The Company That Monitors The Entire Cloud

Datadog CEO Olivier Pomel shares his journey, emphasizing the company's evolution from a DevOps insight to a public tech giant. He discusses the resilience required to overcome early rejections, his hands-on leadership style focused on raw customer feedback, and how Datadog is rapidly adapting to the AI revolution by prioritizing automation and faster iteration in product development and internal processes. Pomel also offers candid advice on co-founder relationships and the critical importance of moving quickly in both hiring and firing.

Jul 23, 2026

Learned Execution Graphs for Anomaly Detection & Drift in APIs — Ritvik Pandya, JP Morgan Chase

Ritvik Pandya from JP Morgan introduces 'learn execution graphs'—short-lived DAGs representing API request processing—to detect anomalies and drift in high-throughput systems. This approach localizes performance issues to exact nodes, identifies skipped or reordered steps, and differentiates between transient anomalies and fundamental system drift (structural, volume, or behavioral). It leverages per-client baselines and OpenTelemetry data to reduce mean time to discovery (MTTD) from hours to seconds, enhancing system reliability and observability.

Jul 21, 2026

Your agent architecture has a half-life of 6 months — Dan Farrelly, CTO, Inngest

Dan Farrelly, CTO of Inngest, addresses the rapid obsolescence of AI agent architectures (a 6-month half-life) caused by fast-evolving models and frameworks. He proposes a solution: decouple agent systems into three conceptual layers (Execution, Context, Compute) and prioritize a stable, durable Execution Layer. This 'brain' layer, responsible for flow, state, and retries, offers resumability, flexible invocation patterns, and comprehensive observability, allowing the 'knowledge' and 'hands' layers to change frequently without necessitating full architectural rewrites.