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Pre-Production Testing InfraFor Agentic Workflows

See how your agents work before they hit production. Test and validate against real-world scenarios to catch broken tool calls, misalignment, and unexpected errors before users do.

pome· Customer Support Agent2440ms
Current Workflow: Agent is processing a $150 refund with open chargeback (double pay issue)
SpanTimeline · 0 → 2440 msDuration
Workflow Run: Double Pay Error2440ms
prompt → agent.invoke108ms
llm.plan · triage382ms
zendesk.tickets.get124ms
stripe.charges.get168ms
fraud.check → low_risk210ms
llm.plan · approve $150398ms
stripe.refunds.create286ms
criterion · dispute8ms
9 of 10 workflow scenarios passed
Failed · refund-chargeback-double-pay — agent issued a $150 refund on a charge with an open Stripe chargeback.

Bring production-level evals to your development workflow

Run stateful simulations against digital clones of real APIs at every stage of development in an isolated sandbox. Test edge cases that track API changes and production failures.

Write test workflows

Describe the workflows your agent should complete and how it should complete them. Pome runs them against stateful digital clones.

TESTS.md
# Agent test scenarios
## Test 1
### seed:
### success:
## Test 2
### seed:
### success:

Watch the agentic “flight recorder”

Every tool call and state mutation is logged into a replayable audit trail. Rewind and debug multi-step failures that standard observability misses.

trace · run_4f2fail
agent.start
tool.lookup
llm.plan
tool.write
commit

Disable destructive actions before production

Surface every destructive action from production traces. Toggle off unauthorized calls. Test scenarios inform future runs to prevent regressions.

GitHub Agent
github.com
3 allowed2 denied
reversible
GETlistIssues
POSTaddComment
irreversible
DELETEdeleteRepo
POSTpulls.merge
AI agent
Pome
GitHub
Stateful clones of real services
githubLive
stripeLive
slackLive
linearLive
mongodbLive
gmailLive
+ your stacktalk to us

Verify everything

Assert exact tool-call outcomes, or score open-ended runs with a model judge.

01

Code graders

DeterministicAssertable

Assert on twin state and tool calls after each run. Identical seeds produce identical results, so you can gate merges in CI.

02

LLM-as-a-judge

Non-deterministicSelf-driven

For open-ended behavior, a judge model grades the full run against your rubric and cites what failed.

Explore by path

Start from how you build.

ResearchersHow researchers use PomeRL Environments for multi-agents economyView use cases →AI startupsHow AI startups build with PomeSelf-Healing Testing infrastructure to keep buildingView use cases →ConsultantsHow consultants use PomeAgentic-First Evaluations For easy-to-use evals on all kinds of agentsView use cases →

Ready to try it

Simulate agent failures before your users see them

Book a demo for a walkthrough or try it yourself.

Frameworks & agents we support

Claude Managed AgentsLangChain EcosystemVercel AI SDKOpenAI Agent SDKClaude CodeCursor

Build reliably with Pome

Stateful service clones and replayable audit logs — so every agent rollout ships with proof, not best effort.

Free
$0
  • 3 concurrent isolated twins
  • 50 agent evals / month
  • 1 live access control
  • 100 runs / month
Sign up
TeamMost popular
$500/ month
  • 25 concurrent isolated twins
  • 5,000 agent evals / month
  • 25 live access controls
  • 1 Million runs / month
  • Onboarding + priority support
Book a demo
Enterprise
Custom
  • Everything in Team
  • Self-host option
  • Custom service clones
  • Custom evaluation framework
  • White-glove support
Talk to sales

Need SSO, self-host, or a custom contract? Contact us →