How to Eval AI Agents — The 2026 Guide

kept by eddie may 28

Evaluating AI agents requires floor-raising error analysis, code-aware offline tests, production monitoring, and a tight feedback loop rather than benchmark-maxxing.

Thoughts on Evals – Raindrop Blog
Production monitoring and A/B testing offer more reliable evaluation of AI agents than offline evals, which fail to capture real-world performance.
also on Braintrust, Raindrop, #ai-agents, #software-engineering, #developer-tools
Reflections on 2025 - Samuel Albanie
AI progress follows compute scaling, making evaluation increasingly difficult but promising radical improvements in infrastructure and economic decision-making.
also on #evaluation, #software-engineering
Introduction | Headroom
Headroom claims 87% token reduction with 100% accuracy by compressing tool outputs, logs, and other LLM context before model inference.
also on LangChain, OpenAI, Anthropic, #ai-agents, #developer-tools
OpenAI's acquisition of OpenClaw signals the beginning of the end of the ChatGPT era
OpenAI's acquisition of open-source agent OpenClaw signals the industry's shift from conversational AI to autonomous agents that browse, click, and execute code.
also on LangChain, OpenAI, Anthropic, #ai-agents