How do you prove an AI agent is viable before funding the full project?
When the outcome is uncertain, do not begin with a full implementation contract. The client provides authorized representative tasks, unacceptable failures, and people who can judge the business result. Wavesteam turns that into a versioned proof of concept with pass thresholds and stop conditions. If critical tasks, risk, or cost miss the threshold, we recommend reducing scope, adding human control, or postponing development.
A PoC is not meant to prove that AI can produce an impressive demonstration. It should answer three investment questions with limited spend: What proportion of the core job can the current system complete? What happens when it fails, and can that failure be detected and recovered? What additional software and operating investment would production require?
When defining model, data, and production boundaries, also compare What components does a production enterprise AI agent need? and Can an AI agent operate ecommerce platforms reliably in production?; the linked guidance adds context that should be considered in the same decision.
Evidence from different decision stages
| Stage | Evidence | Limitation | Appropriate use | Decision |
|---|---|---|---|---|
| Vendor demo or ad-hoc prompting | A few successful examples and intuitive experience | Prompts can be selected, versions are not reproducible, and failure distribution is hidden | Initial capability exploration | Not sufficient for approval |
| Offline PoC | Quality, risk, latency, and cost on a fixed dataset | Does not yet reflect live interfaces and users | Technical feasibility is unknown | Required before major commitment |
| Limited pilot | Results from real users, data, and systems | Requires identity, monitoring, and operational support | PoC has passed | Determines whether to scale |
Turning an ambition into testable tasks
“Build a customer-service agent” is not testable. Wavesteam converts it into tasks such as: “Answer after-sales questions from the current product manual, show a valid page citation, escalate when evidence is missing, and never create a refund without confirmation.” Each task defines expected output, permitted tools, prohibited actions, time limit, and failure handling. High-risk writes are evaluated separately from low-risk reads.
Samples come from authorized historical work and cover frequent and unusual cases, missing and conflicting information, interface errors, unauthorized requests, and adversarial input. Wavesteam decides stratification and quantity from task diversity and failure impact rather than assigning the client a generic “prepare 50–200 questions” exercise. Tuning and final test sets remain separate. The client confirms business representativeness and expert judgments.
Conditions that must remain fixed
Record model and API version, system instructions, knowledge versions, retrieval settings, simulated tool data, run date, and randomness. Keep inputs, outputs, citations, tool traces, latency, tokens, and human scores. When a model or instruction changes, rerun the same test to identify the source of improvement or regression.
Combine automated checks with blinded expert review. Structured fields, tool parameters, and citation positions can be compared automatically. Factual correctness, actionability, and communication risk still require a scoring guide and qualified reviewers. Define fully correct, partially correct, and severe failure, then sample inter-rater consistency.
Pass thresholds and stop conditions
Report task success, critical-fact correctness, valid citations, tool success, appropriate refusal, unauthorized or irreversible actions, human takeover, P95 latency, and cost per successful task. High-impact actions may require zero severe incidents while ordinary Q&A allows controlled human escalation. Do not hide these measures inside one composite “accuracy” number.
Before the PoC begins, Wavesteam submits the gate: which results qualify for a limited pilot, which failures can be controlled by narrower scope or human approval, and which severe errors, cost ceiling, or missing data condition stop the project. The client confirms business risk and investment limits, not model-engineering metrics. A decision not to proceed is a useful PoC outcome when it prevents a larger bad investment.
Wavesteam normally prices and accepts the PoC separately from production development. Deliverables include scope, sample and scoring rules, a reproducible prototype, per-task results, failures, cost estimate, recommendation, and unsuitable conditions. The client's samples, results, and evaluation criteria remain portable so the next decision is not tied to a polished demo.
References
- OpenAI's evaluation best practices covers task-based tests and continuing evaluation; a project should not depend on one provider's tooling.
- The NIST Generative AI Profile supports measurement, risk classification, monitoring, and human governance.
- The OWASP Top 10 for Agentic Applications 2026 helps design tests for goal hijacking, tool misuse, identity, privilege, and autonomy.
PoC findings apply only to the tested version, data, and constraints. Production still requires monitoring for data changes, model updates, and real user behavior.