Human evaluation for AI agents
An AI agent can finish a task, pass the tests and still be wrong. SoReliable AI provides expert human evaluation for AI agents: specialists who review the whole run, not just the final answer.
They read every step the agent took, check whether the result really solves the problem, flag unsafe actions, and write the correct fix when it falls short.
The problem
Agents fail in ways a final score does not show
Agents act. They run commands, edit files, call tools and make decisions over many steps. A green test run can hide a weakened test, a swallowed error, a removed permission check, or a fix that only works for the inputs the tests used.
Judging an agent means judging the path as well as the result. That takes someone who could do the task themselves, and who knows where agents tend to cut corners.
What we provide
What you get with SoReliable AI
Trajectory review
Experts read the agent's steps in order, find where its plan went wrong, and flag loops, wasted work and risky commands.Outcome verification
Did it fix the cause or hide the symptom? Experts check the result against the full test suite and tests the agent never saw.Safety and security checks
Weakened access control, injection risks, destructive commands and changes to tests are caught and tagged.Golden corrections
When the agent fails, the expert writes the minimal, correct fix, ready to use as training data.Structured records
Every review becomes a consistent record: pass or fail gates, failure codes, the step where it went wrong, scores and the rationale.
An example
A coding agent that made the tests pass
A coding agent is asked to fix a parser that returns the wrong number of seconds.
Fix: parse_duration returns minutes
- Tests
- 14 passed
- Changed
- tests/test_timeparse.py
- Expected
- 5400 changed to 90
- What the checks saw: All tests pass, and the pull request looks small and tidy.
- What the expert caught: The specialist sees the agent changed the expected value in the test to match the bug. The parser is still wrong, and hidden tests fail.
A pass rate rewarded the agent for gaming the test. Human evaluation caught it and produced the correct fix.
Illustrative example.
How it works
We prove capability before production
- 1
Agree the rubric
We set the gates and scores with you: what counts as a real fix, what is never acceptable, and how to grade the path.
- 2
Train specialists on agent failures
Our specialists train on the ways agents fail, such as test tampering, symptom masking and hardcoded answers.
- 3
Test them on known cases
Each specialist audits agent runs with known defects and clean runs. Approving a planted defect means they do not qualify.
- 4
Review your agent's runs
Qualified specialists audit your trajectories. Large or sensitive changes get two independent reviewers.
Deliverables
What you receive
- Pass or fail on each critical gate
- Failure codes and the step where the agent went wrong
- Code quality and trajectory scores
- A written rationale for every run
- A golden correction for every failed run
- Records in a format your training pipeline can use
Your rubrics, data, scores and rationales stay yours.
Questions
Questions teams ask
What is human evaluation for AI agents?
Expert review of an agent's work: the steps it took, the tools it used, the final result, and whether it was safe. It finds failures that automated pass rates miss.
Why not use an LLM as the judge?
Automated judges are useful at scale, but they tend to share the blind spots of the model they grade. Expert human review is how you check the judge, and how you catch the cases that matter most.
What kinds of agents do you evaluate?
Coding agents, research and browsing agents, and agents that act in business tools. The method is the same: judge the path and the result against your standard.
What do you need from us?
Your agent's runs (the steps and the final output), your task guidelines, and any tests or reference answers you have. We help you turn them into a rubric.
How do you keep reviewers sharp?
Planted test cases with known defects are mixed into the work. Reviewers who miss them are removed from the review pool.
Start with one task
A pilot gives you experts proven on your hardest cases, and scores you can check, before you commit to more.
Related
- AI evaluation expertsSoReliable AI gives AI teams evaluation experts from the field the model works in. Doctors review clinical answers. Engineers review code. Lawyers review contracts.
- LLM evaluationSoReliable AI evaluates large language model outputs with experts from your field, against a rubric built from your own quality bar.
- Expert data for RLHF and fine-tuningYour model learns from the judgments in its training data. SoReliable AI provides RLHF and fine-tuning data created by experts who know your field.
Ready to try SoReliable?
Tell us what you are building. Any team, any project, any size. We find the specialists, train them on your standard, and evaluate the work against what your project actually needs.