---
variant: detail
kind: inputs
slug: anthropic-agent-evals
url: /inputs/anthropic-agent-evals
title: Demystifying evals for AI agents
source_path: content/inputs/anthropic-agent-evals.md
frontmatter:
  title: Demystifying evals for AI agents
  url: 'https://www.anthropic.com/engineering/demystifying-evals-for-ai-agents'
  source: Anthropic
  consumed: '2026-07-01T00:00:00.000Z'
  note: >-
    Anthropic describes agent evaluations as multi-turn, tool-using,
    state-modifying trials that require tasks, graders, traces, outcomes,
    harnesses, and evaluation suites.
  tags:
    - ai
    - agents
    - evals
    - verification
agent_metadata:
  source_path: content/inputs/anthropic-agent-evals.md
  html_url: /inputs/anthropic-agent-evals
  markdown_url: /inputs/anthropic-agent-evals.md
  source_url: >-
    https://github.com/flaming-codes/thinkinglabs/blob/main/content/inputs/anthropic-agent-evals.md
  summary: >-
    The essay's validation axis draws from this: once an agent acts across many
    turns, the output is not enough. The discipline must inspect traces,
    intermediate decisions, final state, and the harness that made the run
    possible.
  word_count: 109
  approx_token_count: 217
  token_estimate: chars/4
---
The essay's validation axis draws from this: once an agent acts across many turns, the output is not enough. The discipline must inspect traces, intermediate decisions, final state, and the harness that made the run possible.