TESTING STACK GUIDE
Deciding the Right Testing Stack for Your AI Agents
Deciding the Right Testing Stack for Your AI Agents
Deciding the Right Testing Stack for Your AI Agents
Compare the four testing approaches teams use today—and see which ones can expose agent failures before customers do.
Compare the four testing approaches teams use today—and see which ones can expose agent failures before customers do.
3:12
The differences between Chronicle Labs and traditional testing methods.
OUTCOME
A practical breakdown of four agent-testing paradigms—and why dynamic replay offers 30x more scenario coverage than static benchmarks.
The real difference is not how many test cases a tool stores. It is whether the method can exercise complete trajectories, verify tool calls and state changes, and fail safely before production.
Feature / Capability
Catches errors BEFORE production deployment
Validates multi-turn trajectory & tool calls
Validates state mutations on business tools
Zero risk to real customers and enterprise systems
Custom Evals
Limited
Manual updates
Observability
Tests on users
Post-incident
Mock APIs
Unit level
Chronicle Labs
OUTCOME
A practical breakdown of four agent-testing paradigms—and why dynamic replay offers 30x more scenario coverage than static benchmarks.
The real difference is not how many test cases a tool stores. It is whether the method can exercise complete trajectories, verify tool calls and state changes, and fail safely before production.
Feature / Capability
Catches errors BEFORE production deployment
Validates multi-turn trajectory & tool calls
Validates state mutations on business tools
Zero risk to real customers and enterprise systems
Custom Evals
Limited
Manual updates
Observability
Tests on users
Post-incident
Mock APIs
Unit level
Chronicle Labs
OUTCOME
A practical breakdown of four agent-testing paradigms—and why dynamic replay offers 30x more scenario coverage than static benchmarks.
The real difference is not how many test cases a tool stores. It is whether the method can exercise complete trajectories, verify tool calls and state changes, and fail safely before production.
Feature / Capability
Catches errors BEFORE production deployment
Validates multi-turn trajectory & tool calls
Validates state mutations on business tools
Zero risk to real customers and enterprise systems
Custom Evals
Limited
Manual updates
Observability
Tests on users
Post-incident
Mock APIs
Unit level
Chronicle Labs
TRUSTED BY
CUSTOMER PROOF
Trusted by teams scaling thousands of AI agents.
Trusted by teams scaling thousands of AI agents.
Trusted by teams scaling thousands of AI agents.
Reliability becomes real when failures can be replayed, fixed, and verified before customers experience them.
Chronicle Labs gives us the ability to test new agents in a time machine, so our customers never interact with a bad agentic experience
READY BEFORE PRODUCTION
AI fails when conditions change. Make sure yours is ready.
AI fails when conditions change. Make sure yours is ready.
AI fails when conditions change. Make sure yours is ready.
Connect your tools, capture real conversations, and start replaying events for training and evaluation—all from your dashboard.
Connect your tools, capture real conversations, and start replaying events for training and evaluation—all from your dashboard.
Ship and scale AI agents that are proven before production.
© 2026 Chronicle Labs. All rights reserved.
Ship and scale AI agents that are proven before production.
© 2026 Chronicle Labs. All rights reserved.
Ship and scale AI agents that are proven before production.
Company
See how we can help you deploy high quality agents that don’t fail
© 2026 Chronicle Labs. All rights reserved.
