AI Agent Proof of Concept: What to Test Before You Build

An AI agent proof of concept should answer one question: can an agent do this specific job, on your real cases, well enough and cheaply enough to justify building it properly? A useful one takes two to three weeks, typically costs $2,000 to $5,000 on blended rates, and ends with a score against past cases, a list of failures and a go, fix or stop decision. If yours ends with a demo and a round of applause, it has not proved anything. This guide covers what to test, how to set the pass mark, what it costs and how to read the result.

This post is part of our guide to rolling out agents, which starts at wizeb.com/blog/how-to-implement-ai-agents-in-business. If you have not picked a job yet, start with wizeb.com/blog/first-ai-agent-for-business.

The Problem: Demos Are Easy to Pass

A demo uses cases somebody chose. Production uses whatever your customers send on a Monday morning. Modern models are good enough that almost any idea looks convincing in a 20-minute walkthrough, so a demo tells you very little about whether the agent will hold up.

Analysts have been warning about this gap for a while. Gartner predicted in 2024 that at least 30% of generative AI projects would be abandoned after proof of concept by the end of 2025 (gartner.com/en/newsroom/press-releases/2024-07-29-gartner-predicts-30-percent-of-generative-ai-projects-will-be-abandoned-after-proof-of-concept-by-end-of-2025), citing poor data quality, weak risk controls, rising costs and unclear business value. It later predicted that over 40% of agentic AI projects will be canceled by the end of 2027 (gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027). Both are forecasts, not measured failure rates, but the listed causes are the useful part: a proof of concept can test nearly all of them before you commit real money.

Proof of Concept, Pilot or MVP?

Vendors use these words loosely, so agree on definitions before you sign anything.

• Proof of concept: tests feasibility on past cases, with no live customers and no write access to your systems. Two to three weeks.
• Pilot: runs on live work with a small group, in shadow or draft-and-approve mode, to test the agent inside the real workflow. Usually four to eight weeks, and it comes after a passed proof of concept.
• MVP or production build: the hardened version with integrations, monitoring, permissions and handoff rules. Our timeline guide at wizeb.com/blog/ai-agent-implementation-timeline shows how these stages fit together.

What an AI Agent Proof of Concept Should Prove

Write these four questions down before any work starts. Each one needs a number or a clear yes or no.

1. 1Accuracy: how often does the agent get real past cases right, as judged by your staff and not by the builder?
2. 2Failure behavior: when it does not know, does it hand off or does it guess? A wrong answer delivered confidently costs far more than an escalation.
3. 3Data readiness: can the agent reach the information it needs, or does the answer live in someone's head and an unsearchable inbox?
4. 4Unit economics: what does one completed task cost to run, and how does that compare with the staff time it replaces?

Leave out what a proof of concept cannot tell you. It will not show whether staff adopt the agent or how it copes with live volume. Those belong to the pilot, and our guide at wizeb.com/blog/ai-agent-adoption covers the people side.

How to Set the Pass Mark

Set the pass mark before you see any results, in writing. Otherwise a 74% score has a way of becoming "really promising." Start by measuring how your staff perform on the same cases, because few human processes run at 100%, and that baseline is the fair comparison.

• Test set: pull 100 to 200 real past cases, including the awkward ones. Do not let anyone hand-pick only clean examples. Anonymize personal data first.
• Answer key: have two staff members mark what the right outcome was for each case. Where they disagree, you have found an unclear rule the agent will struggle with too.
• Critical cases: flag the cases where a mistake means money, legal exposure or a lost customer. Require zero serious errors on these, whatever the overall score.
• Two thresholds: a go score and a stop score. As a starting point, a low-risk internal task might use 85% to go and 65% to stop, with anything between meaning fix and retest. Customer-facing or financial work needs a higher bar.

A Two-Week Plan

1. 1Days 1 to 2: confirm the one job, the four questions and the thresholds. Name one owner on your side, with three to five hours available that week.
2. 2Days 3 to 4: export the test set and answer key. This is usually the slowest step, and how hard it is tells you something about your data readiness.
3. 3Days 5 to 8: build the simplest agent that could work, connected to read-only copies of your data. No interface polish.
4. 4Days 9 to 10: run the full test set, score it and record the cost per task.
5. 5Days 11 to 12: fix the most common failures, then rerun on the full set and on 20 to 30 fresh cases the builder has not seen. Prompts tuned to one test set flatter the score.
6. 6Days 13 to 14: review the scorecard and make the decision in a single meeting.

What a Proof of Concept Costs

Expect $2,000 to $5,000 on blended or offshore rates, and more like $8,000 to $15,000 at senior US or UK agency rates (about £6,000 to £12,000), since two to three weeks is often 40 to 60 hours of senior time. These are our working estimates, not market survey figures. Model usage during testing is usually small, often under a few hundred dollars.

As a check, keep the proof of concept to around a third of the expected build cost. Our pricing guide at wizeb.com/blog/ai-agent-cost-guide puts a single-task agent at $5,000 to $15,000 on blended rates. If a vendor quotes more for the test than for the thing being tested, ask why. Be just as careful with a free proof of concept: it is usually paid for by a platform commitment or a larger contract later, so ask what you are agreeing to.

Go, Fix or Stop: Reading the Result

• Go: it clears the go score with no serious errors, and the cost per task works. Ask for a fixed quote and move to a pilot.
• Fix: it lands between the thresholds and the failures cluster in one or two patterns, such as a missing data source. Fund one more short round, then decide. Allow only one.
• Narrow: it does well on some case types and badly on others. Cut the scope to the types it handles and route the rest to people. Many good agents start this way.
• Stop: failures are scattered, the answer key itself is disputed, or the task costs more than the staff time it replaces. Stopping after two weeks is a good outcome, because you just avoided a much larger bill.

From AI POC to Production: What Changes

A passed proof of concept is not a nearly finished product. Most of the remaining work is what the test deliberately skipped: live integrations, narrow permissions, monitoring, logging, handoff rules, security review and data agreements. Ask how much of the prototype will carry over. Usually the test set and the prompts survive, and much of the code does not. That is fine, as long as nobody tells you the production build is "almost done."

Keep ownership clean from day one, too. Run the test in your own accounts and keep the test set, since it remains your most valuable asset for testing every later version. If an outside team is doing the work, wizeb.com/blog/outsource-ai-agent-development lists what to keep on your side and which contract terms to agree.

Red Flags in a Vendor Proposal

• No test on your own data, only a demo on theirs.
• No pass mark agreed before the work starts, or the vendor marks its own results.
• A prototype that only runs on the vendor's platform or accounts.
• An open-ended timeline, or a proof of concept that rolls straight into a retainer.
• A results report with a headline accuracy number but no list of failures.

Example: A Proof of Concept That Changed the Scope

Here is a hypothetical example. A 40-person US property management firm wants an agent to triage tenant maintenance emails, which take up about 20 staff hours a week. It exports 150 past emails, has two coordinators mark the correct category and urgency, and sets 85% to go and 65% to stop, with zero misses on emergencies such as gas or flooding.

The first run scores 72% with one missed emergency. The failures fall into two groups: emails with only a photo and no words, and vague messages that the coordinators also disagreed on. In the second round, the agent escalates any email mentioning a safety keyword and sends photo-only messages straight to a person. On the 110 text-based cases left in scope, it reaches 89% with no missed emergencies, at a few cents per email. The firm approves a narrower pilot covering about 70% of its volume, not the full inbox it had in mind. It spends a small sum to learn that before paying for the production build.

How WizeB Runs Proofs of Concept

WizeB builds custom AI agents, workflow automations and voice agents for growing businesses in the US, UK and Europe. We sell this work, so hold us to the same checklist: we agree the job and the pass mark up front, test on your own past cases in your own accounts, and report the failures alongside the score. If the honest answer is stop, we will say so. Learn more at wizeb.com/services/ai-agents.