What Should Be Tested Before Deploying an AI Agent?
AI Systems Beyond the Demo — Campaign 2 | Bamboos Consulting
A successful demonstration establishes that an AI agent can complete a selected workflow under selected conditions. It does not establish that the agent is reliable, authorized to take the actions it selects, or economically preferable to a simpler alternative. For business deployment, the relevant question is not simply whether the agent produces a plausible answer, but whether the entire system behaves acceptably across representative and adverse cases.
This distinction is material. McKinsey’s 2025 global survey found that many organizations were using AI, but most had not yet scaled it across the enterprise. The transition from pilot to operating capability requires explicit evaluation of the workflow rather than informal confidence in a demonstration.
1. Define the task and the alternative
Start with an operational specification: the task, the data available, permitted actions, escalation conditions, acceptable errors, and target business outcomes. Compare the agent against the existing human process and a conventional rules-based or workflow-automation alternative. Agentic autonomy should be justified by measurable incremental value, not by technical novelty alone.
2. Test end-to-end task completion
Measure complete outcomes, not only response quality. An agent may produce an accurate explanation while selecting the wrong tool, passing incorrect parameters, or failing to complete the transaction. A test set should therefore distinguish answer correctness, tool selection, argument validity, state changes, and final task completion. Microsoft’s guidance on agent evaluation similarly separates process and tool-call assessments from final-response quality.
3. Test authorization and action boundaries
Specify which actions the agent may execute independently and which require confirmation. Test unauthorized requests, altered tool responses, malicious retrieved instructions, and attempts to cross permission boundaries. For example, a customer-support agent might be allowed to look up an invoice, but issuing a refund could require explicit authorization and a validated threshold. OWASP identifies excessive agency and tool misuse as distinct security risks in LLM-based systems.
4. Test robustness outside the demonstration path
Include incomplete records, contradictory instructions, unavailable tools, changed inputs, repeated prompts, and atypical workloads. Separate expected cases from held-out scenarios to reduce overfitting to the test examples. Evaluate not just average task success but the nature and severity of failures. In a bounded research setting, Garrone’s 2026 preprint on finite-rule agentic controllers illustrates how local repairs can be re-evaluated on held-out states; the stylized inventory experiments do not, by themselves, demonstrate real-world deployment reliability.
5. Test observability, recovery, and human control
A deployable workflow should permit reconstruction of what was requested, which data and tools were used, what action was attempted, and whether the action succeeded. Test interrupted workflows, retries, duplicate requests, manual intervention, and rollback where technically feasible. NIST’s AI Risk Management Framework emphasizes risk management across the system life cycle. For AI systems legally classified as high-risk under the EU AI Act, relevant obligations can include record-keeping, oversight, and monitoring; those obligations are not universal requirements for every AI agent.
6. Test operational economics and decide using predeclared thresholds
Measure latency, usage costs, operator review burden, incident handling, and sustained task throughput alongside technical accuracy. Define acceptance criteria before running the release evaluation, including specific conditions that trigger rejection, human review, or a narrower deployment. Run a restricted or shadow-mode pilot before expanding permissions. Where an agent does not outperform a simpler alternative under the agreed business metric, the simpler system may be preferable.
A practical release decision
An assessment should produce an auditable answer to six questions: Is the task worth automating? Does the full workflow finish correctly? Are permitted actions respected? Do results survive adverse cases? Can failures be traced and controlled? Does the system produce net operational value? A model benchmark score alone answers none of these questions completely.
At Bamboos Consulting’s AI consulting practice, this is the type of evidence-based distinction that matters when evaluating AI adoption, workflow automation, and the move from prototypes to operating systems.
Sources and further reading
- NIST, AI RMF: Generative Artificial Intelligence Profile (2024; updated online 2026).
- OWASP, LLM06:2025 — Excessive Agency.
- Microsoft Learn, Agent Evaluators for Generative AI.
- McKinsey, The State of AI in 2025: Agents, Innovation, and Transformation.
- European Commission, AI Act obligations by risk category.
- Garrone, R. (2026), Verification of Adaptive Agentic Controllers through Finite Rule Revision, arXiv:2607.09770v2.