TL;DR: Testing an AI agent means checking three different things: whether its replies are correct, whether it stays inside its guardrails, and whether it calls the right tool with the right arguments. ContextQA runs these checks from outside the agent, with no SDK or code access, by pointing at documentation, system prompts, policy files, or conversation logs to generate scenarios, then scoring the result with a mix of deterministic checks and configurable AI judgment.
Definition: Agent evaluation is the practice of testing an AI agent's behavior across a scenario set rather than a single input, covering output correctness, policy adherence, and the tool calls the agent makes along the way.
Quick answers
Does testing an AI agent require SDK access or code changes? No. ContextQA tests from outside the agent, so you point the platform at its documentation, system prompt, policy files, or past conversation logs to generate scenarios, instead of instrumenting the agent's code.
What counts as a guardrail failure? Anything the agent should refuse but doesn't: disclosing PII, approving a refund override it isn't authorized to grant, violating a stated policy, or taking an unauthorized action. The goal is to catch the bypass before a bad actor does.
How is a tool call checked, not just the final answer? By verifying three things about every function call the agent makes: the right tool was called, with the right arguments, producing the right outcome, not just that the final reply sounded correct.
What gets tested, and how
Three dimensions cover most of what breaks in production: the reply itself, the boundaries around it, and the actions it takes along the way.
| Dimension | What it checks | Example scenario |
|---|---|---|
| Responses | Factual correctness, tone, and policy adherence against a source of truth | A customer asks about a $49.99 refund; the reply is checked against the actual order record |
| Guardrails | Safety boundaries the agent must not cross | PII disclosure probes, refund-override attempts, jailbreaks, fabricated pricing or policy claims |
| Tool calls | Right tool, right arguments, right outcome for every function call | Verifying an API call, a SOQL query, or a database write the agent triggered actually did what it claimed |
How this runs without SDK access
Point the platform at whatever already describes the agent: its documentation, its system prompt, its policy files, or past conversation logs. ContextQA generates scenarios from that material, including multi-turn chains of 20 or more turns, and runs them against the agent from the outside. Verification happens the same way a human would check the work: an API call, a SOQL query, or a direct look at database state, not by reading the agent's internals. The output is a regression diff, a confidence score, and an audit trail per scenario, rolled up into an executive summary that exports to a CI dashboard or a read-only shareable link.
A documented example: a Bedrock agent failing a probe
ContextQA's product page shows one worked example against a Bedrock-hosted agent: 4 probes run, 1 failed. That's the shape of what a guardrail run looks like in practice, a small batch of adversarial scenarios, most passing, one catching a real gap, rather than a single pass or fail verdict for the whole agent.
Honest limitations
Judge-based scoring needs a deterministic backstop. Configurable AI judgment is still one model scoring another model's output. Pair it with deterministic checks wherever a verifiable ground truth exists, an order status, a refund amount, an account state, rather than leaving every scenario to judgment alone.
Published numbers are thin so far.ContextQA's own product page doesn't yet publish a sourced figure for how often this catches guardrail bypasses across customers in production, so treat any specific accuracy or volume claim as unverified until it's sourced, and judge the capability on the worked example and the verification methods instead.
Where this fits in the rest of your pipeline
A failed guardrail probe or a bad tool call still needs the same triage any other test failure does. ContextQA's root cause analysis classifies it as a real bug, a test issue, an environment problem, or a flake, rather than leaving it as an unexplained red result. Agent testing sits on the same MCP server as the rest of the platform, so a connected coding agent can trigger a probe run the same way it triggers any other test. If your agent handles voice calls specifically, the checks look similar but the channel is different; see ContextQA's IVR testing and voice AI testing guides. ContextQA's broader platform work has been validated in production too, including an IBM case study on migrating roughly 5,000 test cases into an automated suite.
Do this now
- Pull together what already describes your agent: its system prompt, policy files, and a handful of real conversation logs.
- List the 3 to 5 guardrails that would hurt most if bypassed: PII, refund overrides, and any policy you'd have to disclose publicly if broken.
- Pick 2 to 3 tool calls where a wrong argument would cause real damage, such as a refund amount or an account change.
- Run an initial probe batch and read every failure before deciding whether it's a real gap or a scoring miss.
- Add the deterministic checks (order status, account state) for anything judgment alone shouldn't decide.
- Ready to run this against your own agent? Book a demo.
The bottom line
An AI agent can sound right and still be wrong in three different ways: a bad answer, a crossed guardrail, or a tool call with the wrong arguments. Testing all three from outside the agent, without SDK access, is what turns a demo you trust into a system you can actually ship.
Written by Deep Barot.
