Catching Performance Regressions in AI Agent CI Pipelines
Standard benchmarks miss agent regressions because they grade only final outputs. Trajectory-aware evals in CI catch the 20–40% of regressions that single-turn scoring hides.
Agentic AI, LLM engineering, and the models behind modern automation — multi-agent systems, LLM evaluation and comparisons, RAG, fine-tuning, AI infrastructure, security, and production AI engineering.
From the blog
Standard benchmarks miss agent regressions because they grade only final outputs. Trajectory-aware evals in CI catch the 20–40% of regressions that single-turn scoring hides.
Claude code typescript sdk: a practical engineering deep dive into Claude Agent SDK TypeScript, covering architecture, tradeoffs, and what production teams need to know about TS AI development.
A practical engineering deep dive into Claude Sonnet 4.6 vision, covering architecture, tradeoffs, and what production teams need to know about multimodal AI.
Agno picks up where Phidata left off and ships first-class multimodal agents. Voice, vision, and tools in fewer than 50 lines of well-typed Python code.
Cognee builds and queries a knowledge graph from your unstructured data automatically. A walkthrough from install to your first agent integration in production.
WebArena 2.0 brings real-browser tasks and harder evaluation conditions for browsing agents. The benchmark numbers and what they mean for real production browsing builds.
How ChatGPT Operator 2.0 deployments differ across Toronto, Paris, and Bangalore — local data laws, language quirks, and regional cost economics in 2026.
Where agentic development with Claude Code is heading for non-technical builders — orchestrated agents, reusable skills, and how to prepare now.
The metrics and leading indicators that prove a non-technical PM is shipping well with Claude Code — plus the vanity metrics to ignore.