Build AI features by simulating production scenarios locally.
The best agentic engineering experience to test, build, and optimize multi-turn background agents and data pipelines.
Test AI features end-to-end against real scenarios before deploying.
AI features that use multiple sources of context are notoriously difficult to test without reproducing the original data used in production. Bitfab lets engineers simulate any scenario safely, define outcomes, and have their coding agents make verifiable changes in a loop.
Bitfab · Experimentsrefund-emails · 20 scenariosCapture scenarios from production.
A scenario is everything needed to relive one real customer interaction, captured automatically: the code execution, the database it read, and every external API response.
Every call recorded in execution order with inputs, outputs, and timestamps, two-way serializable down to datetimes, at zero added latency to your production code.
Simulate any scenario during development.
Replay any scenario locally against the database as it was at capture time, with every external call mocked. Or change one detail, a different user, a different order, and test the what-if. Every result is judged against the scenario's outcome, so a pass means what your team says it means.
Handles side effects
- Detects and instruments dangerous calls
- Replays original outputs for safe simulation
- Configurable on the fly by you or your coding agent
Managed scenario data
- Search across billions of scenarios and connect to your monitoring tools
- Adapts scenarios to survive code changes automatically
- Groups similar traces into scenarios that define behavior
Workflows to test, build, and optimize AI features.
With Bitfab, you can build coding agent loops that weren't possible for AI features without simulation or adding your team's judgement. Bitfab provides interfaces connected to your coding agent so your team can define outcomes, review changes, and visualize results.
Fix issues
Start from a failing scenario, simulate it until it passes, and keep it as a regression test.
Run regression tests
Simulate the whole scenario set and catch what broke before you deploy.
Run experiments
Compare before and after across every scenario to see if a change actually helped.
Optimize costs
Profile token spend and iterate to reduce it without losing output quality.
Bitfab learns your team's judgement as you scale.
The scenarios and labels you accumulate become the training data for everything that comes next.
Your team labels what good looks like for each scenario, with the agent doing the heavy lifting.
Scenarios are classified into judges that automate review, tuned on your team's ground-truth labels.
Reuse your scenarios and invariants to train models that fit your product exactly.
One-line install. 15-minute setup.
Works with TypeScript, Python, Ruby, and Go, driven from Claude Code, Cursor, or Codex. Start building methodically now.