Demystifying evals for AI agents
by Anthropic
Stop shipping agents on vibes — build evals that actually measure them.
Overview
'Demystifying evals for AI agents' argues that good evaluations help teams ship agents more confidently and avoid reactive loops where issues surface only in production. It defines the structure of an evaluation—tasks, trials, graders, transcripts, outcomes, and harnesses—and makes the business and lifecycle case for building evals. It explains how to evaluate agents with code-based, model-based, and human graders, and distinguishes capability evals from regression evals. The guide then covers agent-specific approaches for coding, conversational, research, and computer-use agents; handling non-determinism with pass@k and pass^k metrics; an eight-step practical roadmap from initial dataset collection to long-term maintenance; and how automated evals integrate with production monitoring, A/B testing, and human review, plus an appendix on eval frameworks and tools.
At a Glance
- Topic
- Skills
- Level
- Intermediate
- Format
- Guide
- Cost
- Free
- Duration
- ~25 min read
- Provider
- Anthropic
- Hands-on
- No
- Certificate
- None
What You’ll Learn
- ✓The anatomy of an eval: tasks, trials, graders, transcripts, outcomes, harnesses
- ✓When to use code-based vs. model-based (LLM-as-judge) vs. human graders
- ✓How to evaluate agent trajectories, not just final outputs
- ✓How to handle non-determinism with pass@k and pass^k
- ✓A practical 8-step roadmap for building and maintaining agent evals
Highlights
- •Practitioner-grade guidance from Anthropic's engineering team
- •Agent-type-specific eval strategies (coding, conversational, research, computer use)
- •Connects offline evals to production monitoring and A/B testing
Who It’s For
Best For
- ✓Engineers responsible for agent reliability and regressions
- ✓Teams setting up an evaluation harness before scaling an agent
Prerequisites
- •Experience building or operating an LLM agent
- •Basic familiarity with metrics and testing
FAQ
What is Demystifying evals for AI agents?
Anthropic's engineering guide (Jan 9, 2026) on how to evaluate AI agents — the structure of an eval, grader types, agent-specific approaches, and a practical roadmap. For engineers who need to measure agent quality beyond a single accuracy score.
Is Demystifying evals for AI agents free?
Demystifying evals for AI agents is free to access.
What level is Demystifying evals for AI agents for?
Demystifying evals for AI agents is aimed at a intermediate audience. Recommended background: Experience building or operating an LLM agent, Basic familiarity with metrics and testing.
How long does Demystifying evals for AI agents take?
Expect roughly ~25 min read. Most learners work through it at their own pace.
What will I learn from Demystifying evals for AI agents?
You'll learn: The anatomy of an eval: tasks, trials, graders, transcripts, outcomes, harnesses; When to use code-based vs. model-based (LLM-as-judge) vs. human graders; How to evaluate agent trajectories, not just final outputs; How to handle non-determinism with pass@k and pass^k; A practical 8-step roadmap for building and maintaining agent evals.