PagerDuty published a detailed breakdown of their approach to testing AI agent tooling in production, and it's required reading for anyone building or deploying autonomous systems at scale. The core thesis: stop trusting vibes, start running experiments.

The Problem with AI Tooling Evaluation

Most teams evaluating AI agents rely on anecdotal success stories, cherry-picked demos, or gut feelings about performance. This approach falls apart when you're trying to decide between competing frameworks, justify infrastructure spend to leadership, or understand whether that shiny new agent actually improves user outcomesβ€”or just adds latency and unpredictability.

Building a Rigorous Testing Framework

PagerDuty's engineering team advocates for treating AI agent tooling like any other production system: with controlled experiments, statistical significance, and clear success metrics. This means defining baseline performance before deployment, establishing reproducible test scenarios, and running parallel experiments that isolate the variable you're testingβ€”whether that's a new model provider, prompt strategy, or tool-calling architecture.

Measuring What Actually Matters

Beyond traditional software metrics, AI agent evaluation requires tracking task completion rates across diverse input distributions, latency percentiles for end-to-end workflows, error rates categorized by failure mode, and cost per successful task rather than raw API spend. User satisfaction scores correlated with agent-assisted interactions round out the picture.

Key Takeaways

  • Treat AI agent tooling like production systems: run controlled experiments with statistical rigor
  • Define baselines before deployment and establish reproducible test scenarios
  • Track meaningful metrics: task completion, latency percentiles, categorized errors, cost per success
  • Systematic experimentation beats hype-cycle chasing for building reliable autonomous systems

The Bottom Line

The AI tooling space rewards those who experiment systematically over those who follow the hype cycle blindly. PagerDuty's evidence-first approach isn't glamorous, but it's how you build reliable autonomous systems that actually ship value to production.