Anthropic Evaluations

Build rigorous evaluations for AI agents using Anthropic's proven patterns.

Quick Reference

You MUST read the reference files for detailed guidance:

• Grader Types - Code-based, model-based, human graders
• Agent Type Patterns - Coding, conversational, research, computer use
• Roadmap - Steps 0-8 for building evals from scratch
• Frameworks - Harbor, Promptfoo, Braintrust, etc.

YAML Templates:

• coding-agent-eval.yaml - Coding agent template
• conversational-agent-eval.yaml - Support agent template

Annotated Examples:

• Example: Coding Agent - Auth bypass fix walkthrough
• Example: Conversational - Refund handling walkthrough

Core Definitions

Term	Definition
Task	Single test with defined inputs and success criteria
Trial	One attempt at a task (run multiple for consistency)
Grader	Logic that scores agent performance; tasks can have multiple
Transcript	Complete record of a trial (outputs, tool calls, reasoning)
Outcome	Final state in environment (not just what agent said)
Evaluation harness	Infrastructure that runs evals end-to-end
Agent harness	System enabling model to act as agent (scaffold)
Evaluation suite	Collection of tasks measuring specific capabilities

Grader Types (Quick Reference)

Type	Methods	Best For
Code-based	String match, unit tests, static analysis, state checks	Fast, cheap, objective verification
Model-based	Rubric scoring, assertions, pairwise comparison	Nuanced, open-ended tasks
Human	SME review, A/B testing, spot-check sampling	Gold standard calibration

See Grader Types for detailed comparison.

Capability vs Regression Evals

Type	Question	Target Pass Rate
Capability	"What can this agent do well?"	Start low, hill-climb
Regression	"Does it still handle what it used to?"	Near 100%

Capability evals with high pass rates "graduate" to regression suites.

Non-Determinism Metrics

Metric	Measures	Use When
pass@k	At least 1 success in k attempts	One success matters (coding)
pass^k	All k attempts succeed	Consistency essential (customer-facing)

Example: 75% per-trial success rate

• pass@3 ≈ 98% (likely to get at least one)
• pass^3 ≈ 42% (0.75³ all succeed)

Tracked Metrics

tracked_metrics:
  - type: transcript
    metrics: [n_turns, n_toolcalls, n_total_tokens]
  - type: latency
    metrics: [time_to_first_token, output_tokens_per_sec, time_to_last_token]

Attribution

Based on Demystifying evals for AI agents by Anthropic (January 2026).