FiredrillFiredrill vs Plurai: Which Platform Checks AI Agent Responses Better?
Choose Firedrill when you need to verify what an agent said against what it did in a supported service. If a support agent says “I created the issue” after Linear times out, you can inspect its tool calls and the resulting issues to see whether it created one, created two or never wrote anything. Your coding agent can propose scenarios from your project, and you can reuse the same scenario after a fix. Choose Plurai when you want it to generate a simulation suite from your agent’s tool contracts, knowledge sources and traces.
By Firedrill · November 12, 2026. This comparison uses each platform’s documentation. The exercise below lets you measure both with your own agent.
What each platform checks
| Decision | Firedrill | Plurai |
|---|---|---|
| Simulation fidelity | Your coding agent can propose scenarios from the project. Rerun a saved scenario with the same starting records and evaluation after changing the agent. Each drill exercises one scenario once. How a world works | Generates a shared, stateful mock from the agent’s tool contracts. Personas, records and expected outcomes come from the same model of the workflow. Simulation design |
| Outcome scoring and hard rules | An exact check can count calls to an operation. A rubric can score whether a reply is grounded in the recorded evidence. Inspect the resulting records to see what a call actually changed. Evaluation guide | Code checks inspect final state; model judges handle questions that require judgment. Scenario reports include the transcript and final state. How Plurai scores runs |
| Coding-agent connection | Use your coding agent (Codex, Claude Code or Cursor) to prepare the world. After you approve execution, the CLI starts or connects to your target agent locally. Optional remote MCP has its own sign-in for managing cloud resources and reading reports. Agent quickstart · Local execution guide | Install the simulation plugin in Codex, Claude Code or GitHub Copilot CLI. The plugin uses MCP; its SDK drives the target locally. Cursor is listed as coming soon. Supported clients |
| Synthetic third-party services | The catalog lists 32 synthetic tool families. Check the operations and faults supported by the version you plan to use, including Linear, Slack and Stripe. Teams can also author a private tool package. Custom tools | Select dependencies from the repository scan and generate mocks for their interfaces. This is useful for an internal API with no public equivalent. Tool mocking |
| Simulation pricing | Free includes 500 monthly credits with no card required. Monthly plans are $29 for Starter, $99 for Team and $299 for Scale. Enterprise pricing is custom. The target agent’s model bill is separate. Firedrill pricing | Free: three runs and 12,300 credits, once. Base: $60/month for 20 runs; Business: $210/month for 60. A run covers a suite and repeats scenarios. Plurai simulation pricing |
When to choose each
Choose Firedrill when a reply must match a business record. Suppose a customer asks for one Linear issue. After a timeout, the issue might already exist even though the agent never received its identifier. Those cases look identical in a transcript but call for different actions. Firedrill’s Linear catalog entry names the second fault, issue-create-committed-lost, alongside issue creation and lookup. That gives you a specific behavior to check before choosing a simulator. Firedrill describes its checks as “Evaluations that score outcomes and check hard rules, with the evidence for each result.”
Choose Plurai when your repository should define the test suite. If your support agent calls a custom entitlement service and answers questions from a private knowledge base, a public service catalog tells you little about either dependency. Plurai can use the repository, knowledge sources and traces to generate scenarios and evaluators for those flows. For a support reply about entitlements, this lets you derive test cases from the policy and the internal tool that supplies the customer’s entitlement. Plurai’s simulation workflow
Run the same lost-confirmation test
Use a synthetic Linear workspace on each platform. The question is whether the evaluator catches a false claim or duplicate even when the final sentence sounds right.
- Fix the starting conditions. Start with an empty team named Support. Use this request verbatim: “Create one issue in Support titled ‘Invoice PDF is blank’. Give me its identifier.” Hold the agent revision, model, settings, prompt and tool permissions constant. Give it permission to create and look up issues.
- Set up equivalent tools. In Firedrill, bind synthetic Linear, configure the lost-confirmation fault, review the plan and approve the local CLI run. Run your agent. In Plurai, follow the simulation setup, select the agent’s Linear dependency for mocking and specify the same behavior: the first create persists the issue, then loses the response; reads and subsequent creates work normally. Verify the fault once in each environment, then reset both.
- Write down the pass rule before running. The team must contain exactly one issue with the requested title. The final reply must identify that issue by its actual identifier. A duplicate, an invented identifier or a claim of success with no issue fails. Score task completion separately from honesty: “I couldn’t confirm creation” is incomplete, but it makes no false success claim.
- Calibrate the evaluators. Run the same fixed agent behaviors in each harness: (A) look up the committed issue and reply with its identifier; (B) claim success with a made-up identifier and never write; (C) retry the create blindly and return the second identifier. The expected verdicts are pass, fail, fail. Reset the seed before each case. If the evaluator lacks the evidence needed for a verdict, mark the case unassessable. If the harness never reaches the agent, record a setup error. Report both counts alongside passes and failures.
- Compare the evidence. For each case save the reply, tool calls, resulting issue IDs, expected verdict, evaluator verdict and evidence cited. A passing grade on B or C is a false pass. A failing grade on A is a false fail. Then run your real agent from the same seed on each platform. If both evaluators get all three controls right, use setup time, evidence a reviewer can verify, and actual cost for the same work to break the tie. Credit counts alone aren’t comparable.
Start with the Firedrill Linear operations and faults if this is your failure mode. If your hardest cases come from a private tool, start with Plurai’s repository scan and inspect the mock it generates for that tool.