“He said a loop is being built around deepagents for orchestration logic and LangSmith for measurement.”

#8 𝕏 Harrison Chase endorsed the view that agent improvement is harness improvement, with key interventions at the tool boundary: context selection, tool availability, failure recovery, and measurement. He said a loop is being built around deepagents for orchestration logic and LangSmith for measurement.

“Harrison Chase shared that LangSmith’s Messages View helps builders debug agents by replaying conversations and tool calls as the agent experienced them, making traces useful beyond infrastructure teams.”

Harrison Chase shared that LangSmith’s Messages View helps builders debug agents by replaying conversations and tool calls as the agent experienced them, making traces useful beyond infrastructure teams.

“Hamel’s benchmark found automated-eval tools such as Braintrust Loop, Arize Alex, and LangSmith recovered many obvious failures but missed product-judgment failures such as unhandled sales objections; their best-case precision was stated as 80% to 90%, meaning 10% to 20% of flagged errors were not actual errors.”

#1 ▶️ How to Build Better AI Evals with Claude Code in 5 Steps | Shreya & Hamel Peter Yang Shreya uses the free Error Discovery Skill in Claude Code with Opus 4.8 to turn human feedback on AI-generated writing into annotated failure modes, an evaluation rubric, and reusable LLM-judge criteria. The Error Discovery Skill performs five steps: identifies the dataset’s semantic type, designs visual encoding, builds an HTML/Python review app, clusters data and selects diverse samples, then uses an interactive feedback loop to propose new samples and rubric criteria. In the live run, Claude took about 15 minutes to build a three-tab review interface with article-by-article, map/clustering, and progress views; after feedback was supplied, it generated 361 suggested annotations, including 249 instances of the “less than four words staccato” rule. Hamel’s benchmark found automated-eval tools such as Braintrust Loop, Arize Alex, and LangSmith recovered many obvious failures but missed product-judgment failures such as unhandled sales objections; their best-case precision was stated as 80% to 90%, meaning 10% to 20% of flagged errors were not actual errors.

“Harrison Chase shared new LangSmith documentation covering traces, threads, and trajectories—a new concept—and said observability data can support memory and learning, making a clear mental model especially helpful.”

#13 𝕏 Harrison Chase shared new LangSmith documentation covering traces, threads, and trajectories—a new concept—and said observability data can support memory and learning, making a clear mental model especially helpful.

“#3 𝕏 Harrison Chase shared a 20-minute explanation of Managed Deep Agents, which he said had launched the previous day and combines a deep agents harness with managed LangSmith infrastructure.”

GenAI PM Daily August 09, 2026 GenAI PM Daily 🎧 Listen to this brief 3 min listen Today's top 10 insights for PM Builders. Claude Code sessions can now message each other #3 𝕏 Harrison Chase shared a 20-minute explanation of Managed Deep Agents, which he said had launched the previous day and combines a deep agents harness with managed LangSmith infrastructure.

“Harrison Chase demos openwiki, a “dreaming” memory–powered wiki that runs scheduled background jobs to parse LangSmith traces of coding agents and automatically update your codebase documentation.”

#10 𝕏 Harrison Chase demos openwiki, a “dreaming” memory–powered wiki that runs scheduled background jobs to parse LangSmith traces of coding agents and automatically update your codebase documentation. #11 𝕏 Guillermo Rauch built a harness that adds vision capabilities to every AI model, enabling them to process and interpret images seamlessly.

“Harrison Chase launched LangSmith, offering cloud-based sandboxes & deployments, deep‐agent orchestration, and observability tracing. It integrates with hundreds of LangChain models and powers recursive improvement via the LangSmith engine.”

#7 𝕏 Harrison Chase launched LangSmith, offering cloud-based sandboxes & deployments, deep‐agent orchestration, and observability tracing. It integrates with hundreds of LangChain models and powers recursive improvement via the LangSmith engine.

“#4 𝕏 Harrison Chase announces Harbor, a LangChain/​LangSmith integration for running sandboxed evaluations, with self-hosted sandboxes coming soon.”

The newsletter mentions Harbor as a LangChain/LangSmith integration aimed at sandboxed evaluations.

“#19 𝕏 Harrison Chase unveiled LangSmith with three core components—Sandbox for isolated prototyping, LLM Gateway for unified model access, and Observability tools for end-to-end monitoring.”

GenAI PM Daily June 04, 2026 GenAI PM Daily 🎧 Listen to this brief 3 min listen Today's top 25 insights for PM Builders, ranked by relevance from Blogs, X, YouTube, and LinkedIn. Google launches Gemma 4 12B for local multi-step reasoning #19 𝕏 Harrison Chase unveiled LangSmith with three core components—Sandbox for isolated prototyping, LLM Gateway for unified model access, and Observability tools for end-to-end monitoring.

“Harrison Chase breaks down how to evaluate DeepAgents at scale on AWS with LangSmith, covering concrete datapoint and evaluator design methods for longer-horizon agents.”

#2 𝕏 Harrison Chase breaks down how to evaluate DeepAgents at scale on AWS with LangSmith, covering concrete datapoint and evaluator design methods for longer-horizon agents.