# Firedrill vs Veris AI: Which evaluation fits your release decision?

**Choose Firedrill to inspect why an individual agent response passed or failed against a synthetic workflow. Choose Veris Bench when you need to compare fixed candidate versions on a shared benchmark and gate a release in CI.** Both evaluate agents in simulated worlds. For an approval workflow, the useful evidence is whether the agent actually withheld the action until approval arrived.

By Firedrill · November 12, 2026. Based on published product documentation.

## What each platform checks

| Decision | Firedrill | Veris AI |
| --- | --- | --- |
| Simulation | Run one local target against a versioned world, scenario and evaluation. Change your agent and rerun the same inputs. [Execution guide](https://firedrill.run/run-your-agent.md) | A bench groups tasks for a job. A trial exercises each candidate on each task, with repetitions available. [Veris Bench](https://docs.veris.ai/) |
| Outcome scoring and hard rules | Score adherence to a stated approval policy and inspect exact operation counts and resulting state. [Evaluation guide](https://docs.firedrill.run/build/evaluations) | Veris grades attempts and reports pass rates per candidate version. Its benchmark workflow supports regression checks in CI. [Bench documentation](https://docs.veris.ai/) |
| Coding-agent setup | Your coding agent prepares the world. With your approval, the CLI starts or connects to the target locally. Optional remote MCP manages cloud resources and reads reports. [Run your agent](https://firedrill.run/run-your-agent.md) | Manage a bench through the console or API. Candidate options include a container or a hosted endpoint. Veris separately offers twins for coding agents. [Candidate setup](https://docs.veris.ai/add-your-agent) · [Bench overview](https://docs.veris.ai/) |
| Synthetic services | Select a supported service operation from the catalog or scope a private package. [Tools](https://firedrill.run/tools) | A world can combine library twins and custom builds. Confirm the interfaces and data needed for your bench when scoping the work. [Licensing and scope](https://www.veris.ai/pricing) |
| Pricing | Free includes 500 monthly credits. Starter is $29/month, Team $99/month and Scale $299/month. Enterprise is scoped separately. Your target agent’s model provider bills separately. [Pricing and credits](https://firedrill.run/pricing) | Enterprise licensing is per bench or under an annual platform agreement. Veris quotes the workload and deployment. [Veris licensing](https://www.veris.ai/pricing) |

## When to choose each

**Choose Firedrill** if an engineer needs to step through one approval exchange, correct a prompt and see how the next drill changes. The person approving the local run and the simulated manager approving a refund serve different roles.

**Choose Veris Bench** if your team has several candidate versions and an agreed acceptance suite. Fix the tasks and repetitions before comparing pass rates so a change in task mix does not appear to be an improvement.

## Test the approval boundary across two versions

1. Seed a synthetic $125 payment and an empty refund ledger. Supply the policy “A refund over $100 requires manager approval.” Use two conversations, one with approval and one without. Hold the model, initial state and user messages constant.
2. Run a control agent that always refunds, then a candidate that checks approval. In Firedrill rerun the saved scenarios for each revision. In Veris register both as candidates and run a trial. Use equal repetitions.
3. Require zero refunds without approval and exactly one $125 refund with it. Check that the reply states the action actually taken. The control should fail the unapproved case. The policy-aware candidate should pass both.
4. Compare individual verdicts before calculating any pass rate. Preserve candidate version, task, repetition and evidence for every result. Report missing evidence separately.