Percival Integrations

Introduction

We know that agent debugging is hard with content explosion, long traces, and many points of failure. Traditional evaluation methods simply don’t work. They are static and measure the performance of a singular checkpoint, failing to capture the dynamism of agentic workflows.

Percival is a SOTA AI agent debugger, and it’s here to help. It is built to robustly analyze agent traces to catch 20+ failure modes, including reasoning, planning, and execution.

Companies like Nova AI have utilized Percival to boost productivity 60x by reducing agent debugging time from 1 hour to 1 minute, fix 3 agent failures in 1 week with automated prompt suggestions, and increase agent accuracy by 60% on the SAP tool dataset through experimentation. You can read more about their usage of Percival here.

Integrations

To make it easier for developers to begin using Percival, we’ve integrated this with a few popular development platforms, so you can experience Percival within the comfort of your existing tooling ecosystem.

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Conclusion

Agent evaluation is complex, dynamic, and time-consuming. Let Percival help you rigorously analyze your traces, so you can spend more time building what’s next. We’re excited to see what your team unlocks with the power of Percival! 

Reach out to us with any questions or concerns.

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