SimTrace AI CI/CD for AI workflows

Simulate real users to validate end-to-end AI workflows before and during production.

Simulate real users.Catch real failures.Build production trust.

simtrace-workflow.test
1
Generate real user workflows
2
Simulate diverse user behaviors
3
Execute & capture full workflows end-to-end
4
Detect & diagnose failures
Workflow completedAll checks passed

How It Works

A four-step CI/CD pipeline for AI workflows

Generate Real User Workflows, simulate real users, execute end-to-end, then detect and diagnose failures.

01

Generate real user workflows

Identify critical user journeys from real product usage.

Checkout Flow
AI Assistant Chat
Document Analysis
02

Simulate diverse user behaviors

Run web/GUI agents across your product to exercise real journeys.

Running user simulations...

Simulating 247 journeys

03

Execute & capture full workflows end-to-end

Test workflows across AI, UI, APIs, and tools—together.

UI
AI
APIs
Tools
04

Diagnose failures & evaluate business impact

Find where workflows break and why—before release.

Breaking point detected

Tool call sequence mismatch

✗Failure: ambiguous tool call sequence
Breakpoint: submit_order

Challenge

AI systems don't fail at the model - they fail in workflows

  • Multi-step AI workflows break across UI, APIs, and tools
  • Failures are non-deterministic and hard to detect
  • No way to validate real user journeys before release
Isolated evals
Model and agent tests rarely cover full interactions across UI, APIs, and tool execution.
Disconnected benchmarks
Static datasets don't reflect production journeys, edge cases, and user variability.
Late discovery
Observability detects symptoms after impact, without reliable workflow-level success validation.
Unscalable Manual Testing
Manual QA cannot keep pace with evolving AI workflows and multi-system interactions.

What We Do

Reliability testing built for real AI products

Production-grounded scenarios
Generated from real product usage patterns instead of static benchmark datasets.
Workflow-level evaluation
Validate full AI-driven user journeys across UI, APIs, and tool execution.
Simulation-first testing
Run workflow tests before deployment, not after users encounter failures.
Root-cause visibility
Pinpoint the exact breakpoints across AI, UI, and system layers.
Outcome-driven metrics
Optimize for user success rates and measurable business impact.

What You Get

Outcomes your team can measure

Detect workflow failures before production

Understand why AI systems break (not just that they break)

Improve task success rates and reliability

Ship AI features with confidence

Example Insights

Failure traces that read like reality

Concrete failure evidence for the whole workflow.

Failure insight

"Agent got stuck in a loop during checkout"

Failure insight

"User dropped off due to ambiguous AI response"

Failure insight

"Workflow failed after incorrect tool call sequence"

failure_trace.checkout-agent-01
last run: 2m ago
$ simtrace run --suite checkout
[1/4] Generate workflows... ok
[2/4] Simulate real users... ok
[3/4] Execute end-to-end... running
✗ Workflow failed: Agent got stuck in a loop during checkout
Breakpoint: tool call sequence mismatch
- tool: search_products -> ok
- tool: submit_order -> rejected (400)
- tool: retry_submit -> loop detected

Who it's for

Teams shipping AI workflows

AI-native SaaS teams

Validate reliability before production.

AI agent / automation companies

Validate reliability before production.

Teams shipping copilots and workflows

Validate reliability before production.

Free AI Workflow Reliability Audit

We simulate your real user workflows and show:

  • where agents fail
  • where users drop off
  • hidden workflow breakpoints

Ship AI systems that actually work.

Catch AI failures before users do.