A paid proof of concept for an AI agent is a scoped engineering engagement that tests whether a defined workflow can work under agreed business and technical constraints. It should produce running software, evaluation evidence, and a clear recommendation about what follows, with ownership, access, acceptance criteria, and handover agreed before delivery begins.
Begin with a decision the proof must support
The purpose of a proof of concept is to resolve an uncertainty that affects a delivery decision. “Explore AI” leaves that uncertainty undefined. A useful brief asks whether a particular workflow can produce an acceptable proposal using permitted data, pass a meaningful review, and reach a controlled execution path.
For example, a hypothetical team might want to know whether an agent can prepare an internal change request from existing records. The proof would need to show how the request is interpreted, which evidence is retrieved, what a reviewer sees, and what prevents an unapproved change from being applied.
The exit decision might be to proceed, revise the approach, or stop. A finding that the data cannot support the task is useful if it is supported by evidence and identifies the dependency. A polished demonstration without that decision leaves the original uncertainty unresolved.
Agree the scope before building
Name the workflow and draw its boundaries. Identify where the request starts, where the result is recorded, which systems are involved, and which actions are explicitly outside the proof. The team should be able to explain a complete path through the workflow without adding unrelated capabilities.
The scope should distinguish a real integration from a simulated dependency. A test adapter can help investigate behaviour before access is ready, but it cannot establish that the live system's authentication or permissions will work. Mark that distinction in the acceptance evidence and remaining-work list.
Keep a change process for new discoveries. If a required interface is unavailable or the source data has a different structure from the assumed one, make the impact visible. Agree whether to revise the proof's question, resolve the dependency, or stop. Quietly replacing a difficult integration with a demonstration changes what the proof establishes.
Establish access and responsibility
The buyer needs a workflow owner who can explain the task and review whether the output is useful. Engineering needs someone who can clarify the existing system's interfaces. The people responsible for data access and operational changes need to participate where their decisions affect the proof.
Agree the permitted data, environment, credentials, and retention boundaries before the agent can use them. Synthetic or sanitised records may be appropriate for early behaviour checks. If representative data is needed later, the access decision should specify its purpose and scope rather than grant general access to the estate.
Assign acceptance authority explicitly. The person who likes a demonstration may not be the person authorised to accept the deliverable or permit operational use. A named decision owner prevents those different decisions from being confused at handover.
Build a workflow that exposes the hard parts
CodeDTX's Propose–Decide–Execute pattern provides a concrete structure for a governed proof. The agent assembles a proposal with evidence. A named human approves, edits, or rejects it. A separate execution layer applies approved work and records the resulting artefact.
In a test environment, exercise the rejection path as deliberately as the approval path. Inspect what happens when evidence is missing, a reviewer changes the request, or execution cannot confirm its result. The proof needs to expose these conditions so the buyer can assess the design.
Review the workflow through the reference architecture. Agent behaviour, system integration, knowledge access, reliability, safety, and the product interface all contribute evidence. A constrained proof may investigate some areas more deeply than others; the final recommendation should say where evidence is still absent.
This is how the proof connects to enterprise AI product engineering. The output is a tested slice of a business product, with the dependencies and operating responsibilities visible enough to support the next decision.
Define acceptance in observable terms
Write acceptance conditions against what the system does. A proposal should identify the correct record, cite permitted evidence, and preserve uncertainty where the request is ambiguous. A rejected proposal should remain unexecuted. The execution result should match the approved operation and be visible in the audit record.
Include representative cases and exception cases. Agree which failures block acceptance and which unresolved items can be carried into a later phase. Agent evals help examine task behaviour, while integration and application tests check enforceable rules.
Acceptance should also cover the handover. Can the receiving team run the software, reproduce the evaluation, locate a failed request, and understand the configuration? Those checks turn possession of source files into evidence that the work is usable.
Avoid treating an unqualified “production-ready” label as an acceptance criterion. Name the environment, allowed use, and controls actually exercised. A production candidate still needs a release decision that accounts for the operating conditions in which it will be used.
Specify the deliverables you will receive
Agree the deliverable list in the scope so the final review can inspect actual artefacts:
- Running software for the agreed workflow, with setup and configuration instructions.
- Source code and a clear account of dependencies and access requirements.
- Evaluation cases, acceptance checks, and results tied to the tested configuration.
- A review and execution path with inspectable audit records.
- Known limitations and remaining integration or operational work.
- A handover covering how to run, change, test, pause, and troubleshoot the workflow.
Clarify source ownership, repository access, reusable components, and any continuing service dependency in the engagement agreement. Do not assume that paying for a proof automatically settles those terms. The receiving team should know which parts it controls and what access or support it will need afterwards.
Make the next step explicit
A proceed recommendation should identify the evidence that supports it and the work still needed for operational use. That may include deployment controls, a support rota, broader evaluation coverage, or integration with the real approval system. Assign owners rather than treating those items as implied follow-up.
A revise recommendation should say which assumption failed and what a changed approach would test. A stop recommendation should preserve the useful findings, including why the current workflow does not justify further agent development. None of these outcomes requires hiding the limitations to make the engagement appear successful.
If your team will own the capability, involve it during implementation and the final walkthrough. Ask the receiving engineers to run a case, investigate a failure, and make a controlled change. The dedicated AI engineering teams engagement describes capability transfer as part of how work is delivered.
Frequently asked questions
Does a paid proof of concept include production deployment?
Only if production deployment is explicitly included in the agreed scope and its release conditions are met. A proof may establish behaviour in a controlled environment while leaving operational access, rollout, and support work outstanding. The final handover should identify the environment tested and separate demonstrated capability from the remaining requirements for routine use.
What should our team prepare before the engagement starts?
Prepare a named workflow, representative examples, a person who can judge the output, and a route to the relevant system owners. Clarify permitted data and access conditions before implementation depends on them. Bring known exceptions and failed attempts as well as ordinary requests, so the proof investigates the conditions that matter to your organisation.
What if the proof shows that an agent is unsuitable?
The deliverable should explain which assumption failed and include the evidence behind that finding. The next step may be a deterministic workflow, improved source data, a narrower capability, or no further development. Agreeing that a stop decision is valid before work begins gives the team room to report what the proof actually establishes.
How do we avoid depending on the delivery team afterwards?
Agree repository access, ownership terms, dependency documentation, and handover activities before delivery. Have your engineers run the evaluation and troubleshoot a failing case during the engagement. Confirm who owns credentials, deployment, and ongoing support. A useful handover leaves your team able to make a controlled change and verify its effect without relying on undocumented knowledge.
