Evals for Claude Agents: Measuring Quality and Gating Releases (Eight Trends Software 2026)
Build an eval loop for Claude agents — datasets from real failures, programmatic and LLM-as-judge graders, and a CI gate that blocks regressions.
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
Build an eval loop for Claude agents — datasets from real failures, programmatic and LLM-as-judge graders, and a CI gate that blocks regressions.
Secure agentic systems on Claude — sandbox tool execution, enforce least privilege, protect secrets, and defend against prompt injection.
Keep Claude agent runs fast and cheap with prompt caching, the Batches API, model tiering, and context discipline — without losing quality.
Fix the three failure modes that break agentic runs — loops, wrong tool calls, and hallucinated arguments — with trace-driven debugging on Claude.
What to put in a Claude agent's context, what to leave out, and why — practical context engineering that keeps agents sharp, cheap, and reliable in 2026.
A practical guide to connecting MCP servers to Claude agents in 2026 — handling auth, schemas, error handling, and idempotency so tool calls stay safe and reliable.
Code-level patterns for structuring Claude agents in 2026 — shaping prompts, designing tool interfaces, and organizing context so agents stay reliable at scale.
A concrete, follow-along walkthrough for building a production Claude agent in 2026 — from a bare loop to tools, MCP, skills, and a real stop condition.
An end-to-end look at how a Claude agent is wired in 2026 — the model loop, context window, tools, MCP servers, skills, and subagents working together.