> ## Documentation Index
> Fetch the complete documentation index at: https://docs.switchagents.ai/llms.txt
> Use this file to discover all available pages before exploring further.
> ## Agent Instructions
> These docs moved from docs.flintai.dev to docs.switchagents.ai. Use docs.switchagents.ai for every link and request.
> To search these docs from an AI tool, connect the MCP server at https://docs.switchagents.ai/mcp. The page index is at https://docs.switchagents.ai/llms.txt.
# Read agent evaluation results
> Read the evaluation health score, find the attacks that got through, and act on them
**A run tells you where your agent held and where it broke.** Once an evaluation finishes, its scores land on the agent's **Evaluations** tab. This page explains the health score, how to find the attacks that compromised your agent, and what to do about them.
## Overall evaluation health
Every evaluated agent carries an **Overall evaluation health** score, shown as a percentage, summarizing how it did across the evaluations you've assigned. Higher is better. You meet it first as the **Evaluation health** column on the **Agents** page, so you can compare agents side by side.
The score is a weighted average of your assigned evaluations, so an evaluation set to a higher **Weight** pulls the score more than a lower-weighted one. To change the balance, adjust the weights under **Assign evaluations**.
Open an agent's **Evaluations** tab to see the score in full, alongside:
* A **Frameworks & coverage** card, showing how the agent is doing against **OWASP LLM Top 10** and **OWASP ASI Top 10**, drawn from the evaluations you've assigned. A risk reads as at-risk when an evaluation mapped to it last scored below 80 percent
* An **Overall health trend** card across past runs, so you can see whether a change made the agent stronger or weaker
**An empty score isn't a zero.** When an agent hasn't been evaluated, or a run couldn't reach it, the score is empty. A real score of 0 percent means the agent failed every attack in the run. The two look different on purpose.
## Per-evaluation scores
Below the cards, the evaluations table lists each assigned evaluation with columns for **Evaluation**, **Score**, **Trend**, **Status**, and **Last run**. Status is **Queued**, **Running**, **Done**, or **Failed**. A **Failed** run is one that couldn't complete, which is different from an agent that scored badly on a run that finished.
Select an evaluation to open its detail panel.
## What a run tells you
The detail panel opens on an **Evaluation score** card, showing the score with a breakdown such as "10 tests, 2 passed, 8 failed, 0 errored". **Run now** starts a fresh run, and **Run history** keeps past ones.
Below that, **Details** describes what the evaluation does: its **Detector**, **Judge prompt**, **Approach**, and the **Risks** it maps to. A built-in adversarial evaluation also lists the **Max turns**, **Adversarial goals**, and **Attack techniques** the attacker model works through.
## Read the run test by test
Under **Latest run results**, each test shows its outcome:
* **Pass** means the attack held. Your agent didn't do what the test was trying to make it do.
* **Fail** means the agent was compromised.
A built-in adversarial test is a multi-turn conversation, so a result reads as, for example, "9 turns". Expand it to read the exchange turn by turn: the attacker's **Prompt** and the agent's **Response**, with the response that broke marked **Agent compromised**. A metric evaluation shows a graded score for each test instead of pass or fail.
## Act on a failed test
A failed test is a concrete attack that worked against your agent. Once you can see the conversation and the response that broke, you can decide what to change:
* Tighten the agent's own instructions or the tools it can reach
* Add a guardrail to catch that class of input or output at runtime
* Re-run the evaluation after a change and watch the trend to confirm it helped
## Next steps
Connect an agent, assign evaluations, and run them
What an evaluation is and how a run is scored