Aug 15, 2026

Exo: Harnesses should see their own code and logs — Alex Krentsel

An introduction to Exo, a fully recursive AI agent harnessing a novel three-layer architecture (Executor, Harness, Sandbox) designed for autonomous self-improvement. It delves into how Exo surpasses current agent limitations by allowing the agent to edit its own code and policy at runtime, ensuring protected state and isolated execution, and discusses practical implications and the underlying systems philosophy enabling this paradigm shift.

Aug 13, 2026

When to Build Your Own Agent Harness | Harrison Chase, LangChain

Harrison Chase, co-founder of LangChain, delves into the critical role of the 'harness' in LLM agents, explaining how it orchestrates models and context. He covers customizing harnesses with middleware and sub-agents, the trade-offs between off-the-shelf and custom solutions for in- and out-of-distribution tasks, and the importance of evaluations and observability for continuous agent improvement. The discussion culminates in the 'data flywheel' concept and the `LangSmith Engine` for automating agent intelligence through iterative refinement.

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 11, 2026

Agents, codebases, and teams — Aditya Khandelwal, Amazon AGI Lab

Aditya Khandelwal argues that scaling AI agent adoption within engineering teams is a leadership challenge, not an individual contributor problem. He highlights common pitfalls like agent "babysitting" and "slop," and provides a playbook emphasizing progressive disclosure, high-value automation, robust feedback loops, and a critical mindset shift to successfully integrate agents into team workflows.

Jul 29, 2026

Persona Engineering: A Field Guide to AI Synthetic Personas — Ishan Anand, InsightSciences.ai

Ishan Anand discusses synthetic personas for market research, drawing parallels to weather forecasting. He outlines three key failure modes—latent confounders, prompt sensitivity, and difficulty predicting actions—and explores techniques like fine-tuning and calibrated prompting to overcome them. Anand stresses the importance of validating personas against human data using distributional metrics and establishing a "noise floor" based on human-to-human consistency, advocating for their use as economic actors and complements to, rather than replacements for, human research.

Jul 29, 2026

Morgan Stanley's ALPHALAB: Multi-Agent Research Across Optimization Domains — Brendan Rappazzo

Morgan Stanley's AlphaLab is an open-sourced multi-agent system designed to automate quantitative research. Initially, AlphaLab 1.0 automated code generation, backtesting, and experimentation. Facing challenges, AlphaLab 2.0 evolves to prioritize building robust, verifiable environments, which serve as reinforcement learning signals, enabling the system to meta-optimize itself. This shift redefines the human role from performing research to designing these critical environments.