“Harrison Chase says LangChain’s Managed Deep Agents is the easiest way to build and deploy long-horizon agents.”

#10 𝕏 Harrison Chase says LangChain’s Managed Deep Agents is the easiest way to build and deploy long-horizon agents. It’s now in private preview—DM him for access.

“#7 𝕏 Harrison Chase calls LangSmith an org-wide platform for building AI agents that speeds up cross-functional collaboration and tightens feedback loops. #10 𝕏 Harrison Chase argues that treating AI agents as systems to measure and iteratively improve isn’t just a technical challenge—it demands intentional human collaboration and team processes.”

GenAI PM Daily May 10, 2026 GenAI PM Daily 🎧 Listen to this brief 3 min listen Today's top 11 insights for PM Builders, ranked by relevance from X, Blogs, and LinkedIn. PromptLayer’s multi-step agent evaluation framework #1 𝕏 Jason Zhou launched `/goal` support in CodeX and Hermes agents for one-step autonomous coding, advising use of interview mode, clear stop conditions, and a goal-buddy to manage state and goal files. #2 📝 PromptLayer Blog What Is Agent Evaluation? A Practical Guide for AI Teams - Agent evaluation tests whether an AI agent reliably completes tasks across real inputs, edge cases, and new versions by scoring not just final outputs but multi-step behavior via black-box, trajectory, and component-level evaluations, using metrics like task completion rate, tool selection accuracy, unsupported-claim rate, latency/cost per step, and regression pass rate. PromptLayer offers tracing with span-level context, reusable datasets, batch evaluations, backtesting, regression testing, automated evaluation triggers on new prompt versions, and flexible pipelines including code execution, human input, conversation simulation, regex checks, and LLM assertions. #3 in Udi Menkes built his new product’s entire data flow in a single interactive HTML file—complete with diagrams, in-page navigation, and color-coded complexity—letting his team understand it in minutes instead of hours. #4 𝕏 Garry Tan suggests diagramming your AI agent codebases and architecture in plain ASCII, then relentlessly questioning each component to clarify design and accelerate product development. #5 𝕏 Boris Cherny says Claude Code’s switch to a native installer means npm-only stats undercount its real usage. On Thursday it hit its second-highest signup day ever with 15× growth since Jan 1—now you can ask Claude to debug your SQL. #6 𝕏 Boris Cherny is enhancing Claude Code’s UX for snappier performance and adding debug logs so users can self-serve hang diagnostics. #7 𝕏 Harrison Chase calls LangSmith an org-wide platform for building AI agents that speeds up cross-functional collaboration and tightens feedback loops. #8 𝕏 Santiago showcases a step-by-step guide for constructing Python-powered multi-agent systems from scratch, leveraging MCP and A2A patterns to incrementally add complexity and enable collaborative AI agents. #9 𝕏 Garry Tan spends $2K/mo on Openclaw AI tokens to turbocharge product development and startup insights. He’s “tokenmaxxing” now with a goal to make these capabilities affordable for everyone in 18 months. #10 𝕏 Harrison Chase argues that treating AI agents as systems to measure and iteratively improve isn’t just a technical challenge—it demands intentional human collaboration and team processes. #11 in Peter Yang warns that unedited AI-generated markdown can compound small errors over time—what starts as 5% “slop” quickly balloons into an overwhelming pile of confusing, unverified content. Found this valuable? Share it with another PM - they can subscribe at genaipm.com Unsubscribe • Switch to Weekly

“#16 𝕏 Harrison Chase dives into how Ramp Labs built Ramp Sheets and their internal AI agent Inspect—unpacking architecture, features, and real-world use cases—on the Max Agency podcast.”

Harrison Chase is mentioned in the context of a podcast discussion about Ramp’s internal AI products.

“Harrison Chase argues that agent observability in LangSmith is only half the battle—you must embed feedback data collection (and even automated feedback generation) directly into your observability platform to power a continuous AI-agent improvement loop.”

#10 𝕏 Harrison Chase argues that agent observability in LangSmith is only half the battle—you must embed feedback data collection (and even automated feedback generation) directly into your observability platform to power a continuous AI-agent improvement loop.

“𝕏 Harrison Chase cheers on Shashikant’s PyFlue—a Python-native agent harness framework ported from Flue—and invites more developers to dive into harness engineering.”

#10 𝕏 Harrison Chase cheers on Shashikant’s PyFlue—a Python-native agent harness framework ported from Flue—and invites more developers to dive into harness engineering.

“#6 𝕏 Harrison Chase warns that while swapping model providers is straightforward, changing inference/training harnesses is much harder due to vendor lock-in—and urges the development of open harnesses to keep users flexible.”

#6 𝕏 Harrison Chase warns that while swapping model providers is straightforward, changing inference/training harnesses is much harder due to vendor lock-in—and urges the development of open harnesses to keep users flexible. #13 𝕏 Harrison Chase warns that memory and integrations are still tightly coupled to the agent harness—only agents.md and skills hint at any open standard.

“Harrison Chase is excited that LLMs are becoming capable enough to power autonomous web-browsing agents, showcased by the deepagents + @browserbase LangChain integration example on GitHub.”

Harrison Chase is excited that LLMs are becoming capable enough to power autonomous web-browsing agents, showcased by the deepagents + @browserbase LangChain integration example on GitHub.

“#12 𝕏 Harrison Chase walks through building and deploying a simple agent using DeepAgents deploy, inviting PM Builders to try it out (full docs at https://docs.langchain.com/oss/python/deepagents/deploy).”

#12 𝕏 Harrison Chase walks through building and deploying a simple agent using DeepAgents deploy, inviting PM Builders to try it out (full docs at https://docs.langchain.com/oss/python/deepagents/deploy). #19 𝕏 Harrison Chase predicts that by 2026 closed-model costs will be prohibitively high and he’s optimizing deepagents for peak performance on OSS models.

“#21 𝕏 Harrison Chase is launching on May 13th at Interrupt a new feature for LangChain that not only provides testing tools but also guides you on what to test, in what order, and when you’re done.”

#21 𝕏 Harrison Chase is launching on May 13th at Interrupt a new feature for LangChain that not only provides testing tools but also guides you on what to test, in what order, and when you’re done.

“Harrison Chase breaks down the end-to-end infrastructure for deploying long-horizon AI agents—covering task scheduling, state persistence, vector retrieval, prompt templating, and monitoring.”

#11 𝕏 Harrison Chase breaks down the end-to-end infrastructure for deploying long-horizon AI agents—covering task scheduling, state persistence, vector retrieval, prompt templating, and monitoring. #12 ▶️ Hermes Agent: The New OpenClaw? Greg Isenberg Shows how to install and configure Hermes Agent with built-in SQLite memory and 40+ tools via a one-line command, connect to Open Router for transparent token pricing, and deploy it on Android devices using Termox and Termox API.