E-book
The Executive Case for Agent Observability
In 2022, an airline's chatbot gave a grieving customer the wrong answer about a bereavement fare — and a tribunal ruled the company was responsible anyway. The $812 refund was the smallest part of what it cost them.
That's the risk sitting with every executive deploying AI agents today: the gap between having a policy and actually enforcing it in production. Most organizations believe they're covered because they have a safety checklist or a content filter. Few have guardrails that run on all their traffic, catch problems before they ship, or hold up once agents start taking real action instead of just generating text.
Agent observability closes that gap. It's the discipline of evaluating, monitoring, and governing agent behavior continuously — turning trust from something leaders hope for into something they can measure, review, and defend to the board.
Download your copy of The Executive Case for Agent Observability to learn:
- Why sampling a fraction of agent traffic gives you a statistic, not a control — and what it takes to evaluate everything instead
- How to close the three gaps that keep guardrails from holding up in production: untrustworthy evaluation data, deadline pressure, and guardrails too slow or expensive to run at scale
- What eval engineering is, and why it's the same discipline CI/CD brought to software, now applied to agent behavior
- The eight questions every executive should be able to answer before funding the next phase of an AI initiative
