How would you evaluate an AI agent end to end?

Evaluates verified outcomes, trajectory quality, safety, efficiency, recovery, and important slices in realistic environments.

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2CompareCompare with Core answer

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TL;DR

I define task success in terms of a verifiable outcome, then inspect both the final result and the trajectory used to reach it. The evaluation checks tool choice, argument correctness, permissions, evidence, side effects, steps, latency, cost, and termination. Deterministic checks verify external state wherever possible, with model and human review used for judgments they can actually make.

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Practice focus

Agent evaluation, outcome verification, trajectory grading, environment control, and release decisions.

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Answer path

Core answer first, then checkpoints toward a stronger answer.

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Core answer
Start here. This is the direct spoken answer to practice first.
Overview
A plausible final answer can hide unnecessary calls, unsafe attempts, or an incorrect external state.
I define task success in terms of a verifiable outcome, then inspect both the final result and the trajectory used to reach it. The evaluation checks tool choice, argument correctness, permissions, evidence, side effects, steps, latency, cost, and termination. Deterministic checks verify external state wherever possible, with model and human review used for judgments they can actually make.
Checkpoint
Stronger answer
Optional upgrade with examples, trade-offs, and sharper phrasing.
The test environment has stable fixtures, isolated accounts, fake or reversible actions, and reset behavior so runs are comparable. Cases include missing information, tool errors, ambiguous results, prompt injection in tool output, denied approvals, and budget exhaustion. I report success and safety by scenario slice, plus unnecessary actions, intervention rate, recovery quality, and variance across repeated runs.

Review signals

Use these to catch weak phrasing, shallow reasoning, and risky claims.

Common mistakesCandidateCommon mistakesMistakes that weaken the answer or signal shallow understanding.Open details

Details

Grading only the final response text.
Running evaluations against changing shared accounts.
Using one successful trajectory per task.

How would you evaluate an AI agent end to end?

Practice focus

Answer path

Overview

Review signals

Follow-up prompts

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