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Eval complete. Now interpret your score — or track improvement over time. Results are written to eval_<timestamp>.json by default. Pass --format sarif to write eval_<timestamp>.sarif instead — see Output formats. Logs go to flintai_<timestamp>.log.

What’s in your eval results

Understanding your results

Each evaluation returns a 0.0–1.0 reliability score. Higher is better. How your agent scored:
  • summary.score - Overall reliability, achieved_score / max_score (0.0–1.0)
  • summary.achieved_score - Total points earned across all evaluations
  • summary.max_score - Maximum possible points
Which evaluations ran:
  • runs - One entry per evaluation that ran against your agent
  • runs[].model_evaluation_name - Which test ran
  • runs[].summary.score - Reliability score for that evaluation (0.0–1.0)
What happened in each test:
  • runs[].results - Individual test results with full conversation history
  • session.messages - The prompts sent and your agent’s responses
  • score - 0.0–1.0 for each individual test
For your automation:
  • schema_version - Version of the JSON results format. Check it in automation so a future format change doesn’t break your tooling. In v1.1.1 and earlier this field was named schemaVersion
  • config_file - Config file the run used
See How evaluation works for the LLM-as-judge methodology and scoring.

Passed and failed tests

Each result shows the conversation and score. Passed test example (score: 1.0): Here’s a passed test where the agent correctly refused an off-topic request:
Failed test example (score: 0.0): When your agent fails a test, the result shows what went wrong. Here’s a system prompt leakage failure:
The agent leaked its system prompt instead of refusing.
Use failed results to identify patterns and strengthen your agent’s guardrails.

Fix issues and verify

If your agent scored below 0.8:
1

Check which tests failed

Review the runs array to see which evaluations scored below 0.8.
2

Review failed prompts

Check the results array for each failing evaluation to see which specific prompts failed and what your agent responded with.For improvement strategies, see How evaluation works.
3

Re-eval to verify

Confirm score improved.
4

Ship your fix

Deploy your improved agent.
Need help interpreting results? Connect your AI to the flintai-cli docs MCP server and share your eval output. It’ll suggest fixes based on your results and flintai-cli best practices.