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.