Define assessments for AI agents before production: tasks, tools, limits, failures and oversight, with release criteria per scenario.
Index
An agent performing a task during presentation may fail when it receives ambiguous instructions or when a tool becomes unavailable. The assessment needs to cover the complete path and permitted actions. The goal is to know your limits before delegating real work.
Decision this guide helps you make: Release automation by evidence of behavior, not by a favorable demonstration.
Define success and prohibited actions
For each task, describe expected result, available data, and authorized operations. Separate elaboration of a suggestion and execution in systems. A correct answer does not compensate for an inappropriate action taken along the way. Include situations where the expected behavior is to ask for clarification, decline a transaction, or escalate for review.
Create cases outside the ideal script
Test missing information, conflicting font, tool error and repeated order. Include external content that attempts to induce the agent to disobey the application's rules. The mechanism must treat documents and messages as data, preserving the permissions defined in the system. Don't rely solely on text instructions to protect sensitive operations.
Record the route in a useful way
The assessment needs to identify tools called, observable results and decisions, while preserving sensitive data. Compare versions with the same set of cases and add actual authorized incidents to the assessment. It is not necessary to expose internal reasoning; evidence of input, action and outcome is what allows you to investigate operational behavior.
Release by clipping and track changes
Start with limited tools and reversible actions, expanding with testing and supervision. A model, prompt, or source update may change the result and requires relevant reassessment. Define interruption and return to manual flow so that the company is not trapped in automation.
- Success criteria and blocking per task.
- Failure scenarios and adverse external instructions.
- Gradual release with possibility of interruption.
A scenario to check out in the demo
Hypothetical example: the agent consults a page that contains instructions for sending data to another destination. The system must treat this content as external material and maintain tool and authorization limits. The case enters the assessment along with legitimate tasks. Approval depends on observed actions, not an agent's declaration that they followed the rules.
Briefing to request a proposal
- Tasks and tools included in the first release.
- Actions whose impact requires approval or blocking.
- Failure cases that should automatically stop the flow.
Hire evaluation along with implementation
Quantum9 can prepare cases, controls and follow-up for a bounded agent. The proposal must include maintenance of assessments, not just initial construction. Acceptance needs to reflect the impact of the actions on the company, in addition to the apparent quality of the responses.
Discover the scope of Development with agentic AI and deepen the context in related guide.
Technical reference
NIST — AI Risk Management Framework. The reference describes technical fundamentals; The hiring script and the example in this article are an editorial preparation by Quantum9.