An agent PoC succeeds when the go-no-go decision is obvious within 6 weeks. It fails when scope creeps, baseline is missing, or no one is responsible for the call. The 25-item checklist below covers what to confirm before, during, and at the end of the PoC. Companion to pilot program guide, platform evaluation, and procurement checklist.

PoC shape: 4 to 6 weeks, one use case, one vendor

A workable PoC has constrained scope. One use case means one workflow being automated end-to-end, not a "general AI agent". One vendor means depth of evaluation rather than shallow comparison; if you must compare vendors, run two sequential PoCs, not one combined.

Four to six weeks is the standard. Week 1 setup and integration; weeks 2-4 build and run on real data; weeks 5-6 measure, sign-offs, decision. Shorter and you cannot measure quality with statistical confidence; longer and the PoC becomes a stalled rollout.

Section 1: Scope and success criteria (5 items)

  1. Use case in one sentence. "Agent triages and drafts responses for customer support tickets in the [X] category." If you cannot say it in a sentence, the scope is too broad.
  2. In-scope and out-of-scope list. "In: tickets matching [criteria]. Out: tickets involving billing disputes, security incidents, accounts at risk of churn." Vague scope rolls.
  3. Capability success criterion. Measurable. "Agent handles 60 percent of in-scope tickets autonomously to a quality acceptable to support manager review." With the number.
  4. Safety success criterion. Measurable. "Zero responses sent that contain PII not owned by the requesting customer; zero responses that commit the company to actions outside policy; zero financial commitments above $0."
  5. TCO success criterion. Measurable. "Projected cost per ticket at scale of X is below $Y."

Section 2: Data access and baseline (4 items)

  1. Real data, real volume. 100 to 500 representative tasks for build and evaluation. Sampled from the last 90 days, not the last 24 hours.
  2. Baseline measurement. Human-performed: time per task, error rate, error cost class distribution. Without this, no improvement claim survives the CFO.
  3. Data access agreement signed. If the vendor is processing your data, the DPA covers it. If not, the PoC paperwork should be lighter but still documented.
  4. Synthetic mirror for non-PII testing. Some scenarios cannot be tested on real PII. Have a synthetic mirror for those.

Section 3: Security and compliance gates (4 items)

  1. Security pre-review. Vendor SOC 2 or equivalent in hand. DPA reviewed. Tenant isolation confirmed.
  2. Data residency confirmed. Where the data will run during the PoC, in writing.
  3. Audit log access confirmed. You can pull a per-run trace for any decision. Demo the API.
  4. PoC environment scoped. Test or sandbox. Not production. Read-only on real systems wherever possible; writes blocked or audited.

Section 4: Build and integration (4 items)

  1. Minimum viable integration. One source of input, one channel of output. Bigger integrations come after PoC validates the case.
  2. Prompts and tools versioned. Each iteration captured in source control. The version of the prompt the eval ran against is recorded with the eval result.
  3. Build owner named. One person whose calendar is blocked for the PoC. Splitting build across part-time engineers turns 4 weeks into 8.
  4. Daily standups for the PoC team. 10 minutes. Just "what shipped, what is blocked". Skip Fridays if you must.

Section 5: Measurement and evaluation (4 items)

  1. Eval suite defined. 50 to 100 test cases with expected outputs. Built before the agent is functional, so the agent's results are scored against fixed ground truth.
  2. Quality dimensions tracked. Correctness, completeness, safety violations, response time, cost per run. Reported weekly.
  3. Side-by-side comparison. Agent output vs. human-in-the-loop output on the same input. The reviewer is blinded if practical.
  4. User feedback loop. A small group of end users gets the agent's output to react to. Their qualitative feedback shapes the prompt iteration.

Section 6: Go-no-go decision (4 items)

  1. Decision date set on day 1. A calendar-blocked meeting. Cannot be moved without escalation.
  2. Five sign-off owners named. Business, security, procurement, engineering, end-user representative. Each prepares a one-page assessment.
  3. Go criteria documented and weighted. The capability, safety, and TCO success criteria from Section 1. Plus stakeholder go.
  4. Next-step plan ready for both outcomes. If go: production plan. If no-go: archive plan and clear documentation of why.

Timeline by week

Week 0 (pre-PoC). Scope, success criteria, baseline measurement, security pre-review, vendor contract for the PoC, kickoff meeting.

Week 1. Vendor setup, integration build, data access setup. Eval suite defined.

Week 2. Agent functional. First eval run. Iterate on prompt and tool definitions.

Week 3. Agent running on real data subset. Stakeholder review of early outputs. Iterate.

Week 4. Agent on full PoC dataset. Quality metrics stable. User feedback loop active.

Week 5. Final eval run. Stakeholder one-pagers due. Decision packet assembled.

Week 6. Go-no-go meeting. Documented decision. Next-step plan launched.

Common PoC mistakes

Synthetic data. The agent does great on the vendor's demo data, then fails on your real edge cases. Run on real data from week 2.

No baseline. "The agent is X percent better" with no measured baseline is unmeasurable. Spend the first week measuring before building.

Scope creep. "While we're at it, can we also..." Each addition delays the decision and dilutes the result. Park additions for post-PoC.

No safety criterion. The agent shipped a wrong answer in 1 percent of runs. Was that catastrophic or fine? Without a written safety criterion you cannot tell.

Vendor in the room during eval. Vendor advice biases iteration. Engage vendor for capability questions; run eval and scoring without vendor in the loop.

The MIT Sloan study on AI pilot failure rates found that lack of measurable success criteria was the single most common reason pilots failed to convert, present in 60 percent of failed pilots reviewed (MIT Sloan / BCG AI Report, 2024).

FAQ

What is the purpose of an AI agent proof of concept?
To validate that a specific use case can be solved by a specific platform at a specific quality level on real data, before committing to production. A good PoC produces a go-no-go decision in 4 to 6 weeks with measurable evidence.
How long should an AI agent PoC last?
Four to six weeks. Shorter and you cannot measure quality; longer and the PoC becomes a stalled rollout.
What are the success criteria for an agent PoC?
Three measurable criteria: capability target, safety target, and TCO projection at scale.
What goes wrong in agent PoCs?
Scope creep, missing baseline, vendor demos using synthetic data, no clear decision criteria, no stakeholder sign-off plan.
Who should sign off on an agent PoC outcome?
Business owner, security, procurement, engineering, and end-user representative.
What is the right size of dataset for an agent PoC?
100 to 500 real tasks. Less than 50 and you cannot tell signal from noise; more than 1,000 is rarely needed.

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