Agent QA
Find out if AI agents can actually use your product.
Run synthetic agents at your APIs, MCP servers, and web apps. Get trace-backed proof of where they break.
Agent-readiness testing
Can an AI agent finish the job in your product?
We send synthetic agents through your APIs and apps, then hand you the trace of exactly where they got stuck or made things up.
Trace-backed evidence
Ship a product AI agents can finish jobs in.
Know before your customers do whether agents succeed, stall, or hallucinate inside your APIs, MCP servers, and web apps.
Agents are new users
Your product was built for humans. Agents use it anyway.
Agents invent inputs, retry, and misuse tools. Synthians runs them against your product and shows you every break with a full trace.
Agent QA
Prove AI agents can use your product — or can't.
Synthetic agents hit your APIs, MCP servers, and webhooks. Every run comes back with trace-backed proof, not a guess.
Failure modes
Agents fail differently than humans.
They invent inputs
Agents make up enum values, IDs, and parameters you never documented.
They stall on auth
Tokens, logins, and permission flows stop agents cold.
They grab the wrong tool
Vague names and unclear workflows steer agents into risky calls.
Why agents struggle
Agents don't use your product the way people do.
They make up inputs
When a field is unclear, agents invent values, IDs, and parameters that don't exist.
They get stuck signing in
Auth that feels obvious to you can trap an agent in a loop.
They pick the wrong tool
Ambiguous names point agents at the wrong action — sometimes a risky one.
What you'll catch
Catch the failures agents hit before your users do.
Invented inputs, surfaced
Spot where agents fabricate values, IDs, and parameters so you can document them.
Auth blocks, mapped
See exactly where login and permission flows stop agents from finishing.
Wrong-tool calls, flagged
Find the ambiguous names steering agents toward the wrong action.
The blind spot
You can't see why agents fail in your product.
They invent inputs
Agents fabricate enum values, IDs, and parameters your product never defined.
They get stuck on auth
Login and permission flows that humans breeze through stall agents completely.
They choose the wrong tools
Unclear names and workflows push agents toward irrelevant or risky actions.
The reality
Agents will break your product in ways humans never could.
Inputs, invented
Agents conjure enum values, IDs, and parameters that were never real.
Auth, defeated
Tokens and permission flows stop agents the moment they get ambiguous.
Tools, misused
Vague naming sends agents straight to the wrong — sometimes dangerous — call.
Run agents. Read the evidence. Fix it.
From connection to trace in minutes.
Connect your product
Point Synthians at an API, MCP server, web app, or webhook.
Generate agents
Set roles, traits, risk tolerance, and model mix.
Choose the job
Pick a failure-finding preset or write your own workflow.
Fix with evidence
Every run returns a trace that shows exactly what happened.
Run agents through your product and see what they do.
Start with one workflow. We launch the test, capture the live requests and responses, and label anything that falls back.
Connect your product
Add an API, MCP server, web app, or webhook — whatever agents will touch.
Generate your agents
Dial in roles, traits, risk tolerance, tool discipline, and which models they run.
Choose the job
Begin from a failure-finding preset, or write the exact workflow you want tested.
Fix with evidence
When it's done, you get a trace that explains every step the agent took.
Go from connected to trace-backed evidence fast.
One scoped workflow is all it takes to start seeing how agents really behave in your product.
Connect once
Add an API, MCP server, web app, or webhook and you're ready to test.
Generate the right agents
Match real-world behavior with roles, traits, risk tolerance, and model mix.
Target the job that matters
Use a failure-finding preset or script the workflow you care about most.
Fix with confidence
Each run hands you a trace, so you fix the cause instead of guessing.
Stop guessing why agents fail. Watch them try.
Instead of reading cryptic logs, you see the agent's actual attempt — step by step.
Connect your product
Add the API, MCP server, web app, or webhook agents will actually use.
Generate agents
Configure roles, traits, risk tolerance, and model mix to match the real thing.
Choose the job
Start from a failure-finding preset or write the workflow you're worried about.
Fix with evidence
Every test explains what happened, so the root cause stops hiding.
Connect, generate, test, fix — with proof at every step.
No setup marathon. One scoped workflow and you're capturing real agent behavior.
Connect your product
API, MCP server, web app, or webhook — plug it in and go.
Generate agents
Command roles, traits, risk tolerance, tool discipline, and model mix.
Choose the job
Run a failure-finding preset or author the exact workflow to break.
Fix with evidence
Every run returns a trace. No ambiguity, no guesswork.
The workspace
One workspace to test how agents use your product.
Everything for agent QA in one place.
Agent Library
Reusable agents with set roles, traits, and discipline.
Trace Viewer
Every step of every attempt, in order.
Reports
What worked, what broke, what fell back.
Automation
Re-run as your product changes.
What's inside
A focused workspace for testing how agents use your product.
Each piece does one job well, so you spend time fixing — not wrangling tools.
Agent Library
Keep a set of synthetic agents you can reuse across tests.
Trace Viewer
Walk through exactly what an agent did, step by step.
Reports
See where agents succeeded, failed, or had to fall back.
Automation
Run your tests again whenever your product changes.
From break to fix
Tools that turn agent failures into fixes.
Each part of the workspace moves you from “something broke” to “here's the fix.”
Agent Library
Reuse proven agents so every test starts faster.
Trace Viewer
Pinpoint the exact step where an agent went wrong.
Reports
Turn raw runs into a clear list of what to fix.
Automation
Catch regressions before they reach real agents.
Beyond the logs
The visibility your logs never gave you.
When an agent fails, these four views tell you why — without the log-diving.
Agent Library
Stop rebuilding test agents from scratch every run.
Trace Viewer
See the exact step that broke instead of guessing.
Reports
Read what happened without parsing raw output.
Automation
Stop manually re-testing every time you ship.
The workspace
Built to expose exactly how agents use your product.
Four sharp tools. No dashboards full of noise.
Agent Library
A reusable arsenal of synthetic agents, ready on demand.
Trace Viewer
Every action an agent took, captured in full.
Reports
Clear verdicts on what succeeded, failed, or fell back.
Automation
Re-run the moment your product changes. Every time.
First run in minutes
See what breaks when agents use your product.
One workflow. A few minutes. A full trace.
Run your first testTrace-backed evidence
Curious what agents actually do in your product?
Start with one scoped workflow and read the trace for yourself.
See your first traceAgent-ready
Know your product is ready for AI agents.
Get trace-backed proof of where agents succeed and where they stall.
Get your agent reportRedacted traces
Find what breaks before real agents do.
Every test explains what happened — so you fix the cause, not the symptom.
Find what breaksTrace-backed proof
Prove your product works for agents. With evidence.
Run synthetic agents at it and let the traces settle the question.
Test agent readiness