CAPTIVOLT INSIGHTS
Executive summary
AI agents need more than output checks. Enterprises must evaluate task completion, tool-use accuracy, instruction adherence, escalation behaviour, safety, cost, latency, and auditability before agents enter production. An agent that writes beautiful prose but calls the wrong API is not a quality problem — it is an incident.
The problem
Traditional QA asks: is the output correct? Agents demand a harder set of questions: did it complete the task, did it choose the right tool with the right inputs, did it follow instructions when instructions conflicted with convenience, did it escalate when it should have, and can you prove all of this afterwards? Most teams discover these questions after the first production incident, which is the most expensive possible time to discover them.
A practical framework
- 01
Task completion: did the agent achieve the intended outcome across representative scenarios, including edge cases?
- 02
Tool-use accuracy: right tool, right parameters, right sequence — measured, not assumed.
- 03
Instruction adherence: does the agent respect constraints under pressure, ambiguity, and adversarial input?
- 04
Escalation behaviour: does it hand off to humans at the defined thresholds, every time?
- 05
Safety and policy checks: prompt injection resistance, unsafe-output handling, data-boundary respect.
- 06
Cost and latency: per-task economics measured against thresholds before scale, not after the invoice.
- 07
Auditability: every decision, tool call, and escalation logged in a form governance can use.
Key takeaways
- Agent evaluation is multi-dimensional; output checks alone are negligent.
- Define escalation thresholds before build, then test against them.
- Cost per task is an evaluation dimension, not an afterthought.
- If you cannot audit it, you cannot deploy it in a regulated environment.