Replay recorded scenarios and flows
Re-run workflows captured in collected business data to reproduce real interactions, debug failures and compare agent behavior.
For AI studios, agencies & consultants
A sandbox for building production-ready
AI agents
One platform to build robust agents, replay real workflows and stress-test behavior before deployment.
01 / Business environment
Simulated business environment
modeled on real data
Design, develop and test agents inside a complete simulated business environment built from your clients’ workflow data, with connected systems, people and events.
APIs
Read & write
Webhooks
Trigger callbacks
Databases
Query records
Events
Change state
Employees
Test handoffs
Clients
Handle requests
Simulate new scenarios and edge cases
Use the same business data to generate realistic new flows, changing conditions and edge cases your agent has never encountered.
Hands-on working time
Estimated time saved
49%
136 → 70 team person-hours
One workflow · Three apps
Twenty scenarios · Three benchmark configurations
Baseline (100%)Time with simulation
- Estimated conventional working time 12 person-hours; simulated environment 5 person-hours.
Data collection
58% less time
- Estimated conventional working time 24 person-hours; simulated environment 8 person-hours.
Environment setup
67% less time
- Estimated conventional working time 40 person-hours; simulated environment 32 person-hours.
Agent development
20% less time
- Estimated conventional working time 8 person-hours; simulated environment 3 person-hours.
Demo preparation
63% less time
- Estimated conventional working time 24 person-hours; simulated environment 8 person-hours.
Scenario testing
67% less time
- Estimated conventional working time 16 person-hours; simulated environment 10 person-hours.
Debugging and tuning
38% less time
- Estimated conventional working time 12 person-hours; simulated environment 4 person-hours.
Regression benchmarking
67% less time
Forecast · Sources & assumptions
Illustrative forecast, not measured ActOut performance: 136 → 70 team person-hours for one workflow, three apps, twenty scenarios and three benchmark configurations. This is hands-on effort, not elapsed delivery time.
Assumes supported integrations, reusable fixtures and automated runs. Data review, fidelity checks, core logic, fixes and human evaluation remain included. Waiting, production rollout and real-user acceptance are excluded. Custom integrations or incomplete data increase effort; validate against your own baseline.
Research informs the approach, not these figures. The customer cases are vendor-published; the Forrester study is a commissioned composite.
02 / AI testers
Test your agent with hundreds of realistic AI testers
Run usability, load and adversarial tests with hundreds of AI testers modeled on real business data. Choose your mix of users, prospects, customers, employees, suppliers, partners, external agents and bad actors. Catch broken flows, weak responses and unsafe behavior before deployment.
Plan an agent test03 / Protected data
Start with real business data, protected locally.
Run the open-source collector on your own infrastructure. Connect business apps and replace sensitive details locally while preserving relationships and useful context.
Open-source collector (opens in a new tab)Local obfuscation
- Name
- Jamie Carter
- Alex Morgan (protected)
- jamie.carter@example.test
- alex.morgan@example.test (protected)
- Phone
- +1 415 555 0138
- +1 415 555 0164 (protected)
Protected locally, before export
Context and relationships stay intact.
Connect the tools your agents work with
04 / Monitoring & improvement
Keep improving with every real-world run
Bring live responses, tool calls and outcomes back into the sandbox. Find weak spots, reproduce failures and test fixes as your clients’ workflows change.
- 01
Collect live data
Capture conversations, tool calls, outputs and business outcomes.
- 02
Cluster scenarios & use cases
Group related workflows and identify recurring patterns in agent behavior.
- 03
Find new & underperforming cases
Surface failed tasks, weak responses and scenarios missing from your tests.
- 04
Update the simulation
Bring those cases, records and user profiles into a reproducible test environment.
- 05
Develop & retest
Debug the failing steps, compare fixes and run the regression suite.
Deploy the improvement. Keep monitoring.
See what needs work
Separate new requests from recurring failures. Review the response, output and outcome signals behind each case.
Fix faster in simulation
Turn failures into repeatable tests and feed them into your debugging workflow. Compare changes under the same conditions.
Expand use-case coverage
Keep new scenarios as regression tests, so each fix improves coverage for the next client release.
From the ActOut team
Engineering notes & publications
Agent evaluation, simulation, and the systems we build.
Why demos survive and deployments struggle
What changes when an agent meets real customers, messy records and an ordinary Monday.
Read article
When the customer changes the plan
How to handle corrections, implied needs and requests that evolve mid-conversation.
Read article
A closed chat is not a solved problem
Measure the customer’s outcome, including everything that happens after the chat.
Read article
Tests that change what you ship
Choose cases and measures that help your team make a real release decision.
Read article05 / Your test environment
Let’s book a call
with our FDEE Forward Deployed Evaluation Engineer
We’ll help you understand your agent’s testing and evaluation needs, and suggest a simple setup to get started.