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Do not treat a polished AI agency demo as proof that the system is ready for your business. Treat it as a hypothesis: a scripted example that must be reproduced on your data, difficult cases, and known exceptions. Before signing, define measurable acceptance thresholds, rerun the workflow without hidden manual preparation, inspect how failures are handled, and verify privacy, security, subcontractors, cost assumptions, and your ability to leave. Require dated evidence and a human escalation route. There is no universal pass/fail score; a support bot, claims workflow, and clinical decision aid carry different tolerances for error. The right question is whether this specific system performs the agreed job, under your controls and risk limits, at a total cost and contractual commitment you can accept.
What does an AI agency demo actually prove?
A demo proves only that someone produced a favorable result in a particular environment. It may use selected inputs, a prepared knowledge base, an operator correcting errors off camera, or a workflow that is narrower than the one you need. It does not establish production accuracy, reliability under load, safe behavior on unusual requests, or compliance with your obligations.
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Ask the agency to label every part of the demonstration: the exact input, system version, prompt or rules, retrieval sources, tools, integrations, human actions, and post-processing. Record the date and environment. If a person edited the input, chose a better result, or removed an unsafe output, that intervention is part of the demo and must be disclosed.
Use the demonstration to decide what to test next. A credible supplier welcomes repeat runs, shows an unedited failure, and explains what is configurable versus fixed. A weak supplier changes the script when you ask for representative work or relies on adjectives such as autonomous, enterprise-grade, or near-perfect without a defined metric.
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How should I define the job before testing?
Write a one-page job definition before another meeting. Without it, the agency can optimize for a spectacular example while you evaluate an undefined promise.
Describe the users and workflow
- Name the people or systems that will submit requests and the people who may act on an output.
- List every input type, including attachments, structured fields, images, languages, and expected volume.
- Describe the current process step by step, including manual reviews, queues, approvals, and hand-offs.
- State the desired improvement: shorter handling time, fewer errors, higher throughput, better consistency, or another measurable outcome.
Set acceptable and unacceptable outcomes
Define a metric for each material output. Examples include correct classification against an approved label set, extraction fields matching a verified record, or a response that cites the approved policy. Set a minimum acceptable rate, a maximum tolerable error rate, a response-time limit, and a required escalation rate. Separate errors that are inconvenient from errors that could cause legal, financial, safety, privacy, or reputational harm.
List assumptions and exclusions
Ask the agency to identify dependencies such as clean source data, a particular software version, a connector, a retrieval index, or human approval. Put exclusions in the test plan. If the system cannot process handwritten forms, unsupported languages, or records older than a certain date, that boundary belongs in the commercial proposal rather than being discovered after purchase.
How do I run a fair, repeatable demo test?
Use an approved test set and insist that the agency runs the same workflow twice. The second run reveals whether the result was luck, hidden preparation, or an unstable model configuration.
- Approve the data. Select normal cases, difficult cases, and known exceptions from your real work. Remove or mask information that the agency is not authorized to receive, and document any changes that could make a case easier.
- Freeze the script. Attach the user instruction, input format, model or service version, retrieval sources, tools, and expected output format to the test record. Do not allow silent prompt edits between cases.
- Run without backstage work. Require the agency to show how files are ingested, how context is selected, and when a person intervenes. If manual preparation is permitted, measure and price it as a separate process step.
- Use a blind review where practical. Have someone who did not build the demo compare outputs with a verified answer or an agreed rubric. Record partial credit, unsafe content, missing citations, and refusals instead of counting only successful examples.
- Repeat the workflow. Rerun a representative sample under the same conditions. Compare output changes, latency, tool calls, and escalation decisions. Ask the agency to explain any material variation.
- Preserve evidence. Keep the input identifier, timestamp, output, confidence or uncertainty signal, reviewer decision, error category, and system configuration. A screen recording alone is not an evaluation.
For a pilot, agree in advance who owns the test set, who may see raw data, how long logs are kept, and whether results can be used to train a model. Do not let a pilot silently become production use.
Which edge cases and failure modes should the agency show?
A trustworthy demo includes bad inputs and recovery, not just a smooth path. Ask the agency to run each scenario and explain the expected control.
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Missing or conflicting information
Remove a required field, provide two records with different values, or supply an outdated policy. The system should identify the gap, avoid inventing an answer, and route the case for clarification or review.
Ambiguous requests
Use a request with two plausible interpretations. Check whether the system asks a focused question, states its assumption, and records the choice. An answer that sounds confident but selects the wrong interpretation is a high-risk failure.
Low-confidence and out-of-scope work
Ask for an unsupported language, file type, product, or policy. The expected behavior may be a refusal, a clear limitation message, or a human hand-off. Require the agency to define the trigger and the destination of that hand-off.
Unsafe or adversarial inputs
Test prompt injection in retrieved documents, malicious attachments, requests for restricted data, and attempts to make the system bypass approval. Observe whether tool permissions, output filters, and review gates still apply.
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Submit a deliberately wrong answer for correction. The agency should show how a reviewer fixes it, whether the correction affects only the case or future behavior, and how the change is logged. Ask how you can reconstruct who saw the data, which source was used, and which version produced the output.
What parts of the AI solution must be separated?
Request an architecture and responsibility map rather than a single label such as AI platform. Your risk and negotiating position depend on each component.
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| Component | Questions to ask | Why it matters |
|---|---|---|
| Model | Which model and version are used? Can it change without notice? Is it hosted by the agency or another provider? | Model changes can alter accuracy, cost, latency, and data handling. |
| Prompts and rules | Who writes and maintains system instructions, validation rules, and safety policies? | Hidden instructions can be a dependency and may be difficult to port. |
| Retrieval sources | Which documents are indexed, how are permissions enforced, and how are updates and deletions propagated? | Stale or unauthorized context can produce wrong or disclosive answers. |
| Tools and integrations | What systems can the agent read or change? Are actions reversible and approval-gated? | Tool access determines the potential blast radius of an error. |
| Human review | When is a person required, where are reviewers located, and what service level applies? | A claimed autonomous rate may conceal an essential labor cost or a safety control. |
| Post-processing | What code formats, filters, ranks, or edits the model output? Who owns that code? | Failures may originate outside the model and may be missed in model-only metrics. |
| Subcontractors | Which vendors operate hosting, monitoring, annotation, support, or data transfer? | Third parties affect jurisdiction, security, continuity, and contractual remedies. |
Ask what you can configure yourself and what is controlled by the agency or its subcontractors. Require notice and an approval or exit right for material component changes.
What evidence should an agency provide before I believe its claims?
Request an evaluation pack with enough detail for an independent reviewer to reproduce the conclusion. Treat claims about accuracy, speed, savings, and autonomy as propositions to verify, not as established facts.
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- Evaluation design: the task definition, rubric, pass criteria, and treatment of partial or unsafe answers.
- Test population: the number and source of cases, the mix of normal, difficult, and exception work, and exclusions.
- Dates and versions: run dates, model and software versions, configuration, and any material changes between runs.
- Metrics: accuracy by case type, false-positive and false-negative rates, escalation rate, latency distribution, tool failures, and reviewer effort.
- Baselines: results from your current process or a defined alternative, measured on comparable cases.
- Limitations: known failure modes, unsupported inputs, confidence calibration, and conditions under which performance falls.
- Raw records: anonymized inputs and outputs, reviewer decisions, and logs sufficient to audit the reported numbers.
Ask whether the headline figure is an average, a best case, or a one-time result. A high overall rate can hide unacceptable performance on the exceptions that matter most. UK Information Commissioner’s Office guidance on AI contracts and third parties advises due diligence on accuracy, bias, and design trade-offs before procurement; use that principle to demand evidence tied to your own risk profile.
How do I check privacy, security, and data-use terms?
Do not sign until the data flow is documented and the legal roles are clear. Verify each item with the agency’s contract, security documentation, and, where appropriate, your own privacy or security team.
Data governance questions
- Are you the controller, the agency a processor, or do roles differ by feature and jurisdiction?
- What purposes are permitted: providing the service, abuse monitoring, troubleshooting, analytics, model improvement, or something else?
- Which data categories are processed, including personal, confidential, regulated, or customer-provided information?
- In which countries are data, backups, support tickets, and telemetry stored or accessed?
- What are the retention periods for inputs, outputs, prompts, logs, backups, and human-review records?
- How do you request deletion, and how is deletion propagated to caches, indexes, backups, and subcontractors?
- Can the agency or a model provider reuse your data for training or evaluation? If so, is consent explicit and can you opt out?
Security and third-party controls
- Is data encrypted in transit and at rest, and how are keys managed?
- How are identities, privileged accounts, service accounts, and tenant boundaries controlled?
- Are access events, tool actions, configuration changes, and exports logged and available for audit?
- Which subprocessors are involved, what do they do, and how are changes notified and approved?
- What is the incident-notification deadline, investigation process, evidence package, and customer communication plan?
- How are dependencies, vulnerabilities, and supply-chain risks assessed and remediated?
Match controls to the jurisdiction and data involved. A general security statement is not a substitute for a processing agreement, documented retention rule, access review, and a tested deletion or incident process.
What should the contract promise after the demo?
Attach the demo script, test description, and agreed evidence to the contract or statement of work. The contract should convert a persuasive presentation into enforceable responsibilities.
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| Contract area | Write down |
|---|---|
| Scope and deliverables | Workflow boundaries, integrations, environments, documentation, training, and measurable milestones. |
| Responsibilities | Who supplies data, validates outputs, maintains source content, approves actions, and handles escalations. |
| Performance KPIs | Acceptance thresholds by case type, latency or availability targets, review rates, measurement method, and test frequency. |
| Privacy and security | Roles, permitted purposes, locations, retention, deletion, encryption, access controls, logs, subprocessors, and incident duties. |
| Pricing assumptions | Usage units, model or token charges, storage, connector fees, human review, support tiers, overages, implementation work, and change-order rates. |
| Intellectual property | Rights to your data, outputs, prompts, configurations, evaluation records, custom code, and documentation. |
| Portability | Export formats, prompt and rule access, knowledge-base export, log access, API documentation, and assistance migrating away. |
| Change control | Notice periods, approval rights, regression testing, and remedies when a model, subprocessor, feature, or price changes. |
| Support and continuity | Support hours, severity definitions, response and resolution targets, backup arrangements, and disaster recovery responsibilities. |
| Exit and remedies | Data return and deletion certificates, transition help, refunds or service credits, suspension rights, and termination if acceptance criteria or security duties are missed. |
Define what happens when a KPI is missed: a correction plan, additional testing, service credit, withheld milestone payment, or termination. A promise to use commercially reasonable efforts is not equivalent to an acceptance test with a measurable remedy.
How can I compare two AI agencies fairly?
Compare evidence and obligations, not presentation quality. Use the same test set, scoring rubric, and contract questions for every bidder.
| Comparison axis | Evidence to compare | Decision question |
|---|---|---|
| Evidence quality and reproducibility | Raw records, repeat runs, dated versions, independent review, and disclosed limitations. | Can another reviewer reproduce the claim? |
| Workflow fit and edge-case coverage | Results on normal, difficult, exception, ambiguous, unsafe, and out-of-scope cases. | Does it work where your risk is concentrated? |
| Privacy, security, and third parties | Data map, roles, locations, retention, controls, subprocessors, and incident terms. | Can you lawfully and safely use it? |
| Explainability and portability | Source citations, logs, correction paths, exports, APIs, and documentation. | Can you investigate, govern, and replace it? |
| Total cost and support | Implementation, usage, human review, support, overages, change fees, and exit assistance. | What will the complete operating model cost? |
Score each axis against thresholds you set for the workflow. Do not average away a fatal weakness: a system can be inexpensive and accurate yet unacceptable if it cannot delete regulated data or provide a required human review.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Is there a universal pass or fail score for an AI demo?
No. Risk, error tolerance, data sensitivity, and the consequence of an automated action differ by workflow. Set a written go, pilot, or no-go rule for each material requirement.
- Go: every critical threshold is met, evidence is reproducible, controls are documented, and contract remedies are acceptable.
- Conditional pilot: noncritical gaps have owners, deadlines, restricted data and permissions, a human approval gate, and a budgeted exit.
- No-go: the agency refuses representative testing, cannot explain data use or subcontractors, hides manual work, or misses a critical safety, privacy, or performance threshold.
Have the business owner, security and privacy reviewers, procurement, and the people who will operate the workflow sign off on the decision. UK guidance also emphasizes that suppliers should identify bias and that a procured system must align with the buyer’s chosen trade-offs; document those trade-offs rather than relying on a vendor’s generic score.
What are the warning signs, and what should I do if the demo fails?
Warning signs include refusal to rerun a case, unexplained prompt or model changes, selective examples, a claim of full autonomy with undisclosed review, pressure to upload live sensitive data, vague subprocessors, and pricing that excludes usage or human labor.
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If a test fails, classify the failure before negotiating a fix. A data-quality issue may require a validation step; a retrieval issue may require source permissions and update controls; a model error may require a narrower scope or mandatory review; an integration failure may require a different tool permission. Ask for a corrected run on the same failed case and a fresh sample. Do not accept a new success story as proof that the original failure is solved.
Pause the purchase when the agency cannot provide a dated explanation, owner, and retest date. Keep the failed evidence with the decision record. If you proceed with a limited pilot, reduce permissions, prohibit irreversible actions, cap data and spend, and write the stop conditions into the pilot order.
What is the practical pre-signature checklist?
Use this checklist in the final review meeting and attach the completed answers to your procurement file.
- The job, users, inputs, outputs, baseline, target improvement, exclusions, and assumptions are written.
- Normal, difficult, exception, ambiguous, unsafe, and out-of-scope cases have been tested with approved data.
- The workflow was rerun without hidden preparation, and repeatability and reviewer effort were measured.
- Failure handling, confidence signals, human escalation, correction, logging, and audit access were demonstrated.
- Model, prompts, retrieval, tools, integrations, human review, post-processing, and subcontractors are identified.
- Evaluation design, population, dates, versions, metrics, baseline, limitations, and raw records are available.
- Controller or processor roles, purposes, locations, retention, deletion, access, encryption, logs, subprocessors, incidents, and data reuse are agreed.
- Scope, responsibilities, KPIs, prices and overages, support, intellectual property, portability, change control, and exit remedies are contractual.
- Critical thresholds have named owners, and the decision is go, conditional pilot, or no-go.
Frequently Asked Questions
Should I let an agency test with live customer data?
Only after your privacy and security review authorizes the purpose, location, access, retention, and deletion controls. Start with synthetic or masked records when they can answer the test question, and use the smallest live sample needed for a defined pilot.
How many test cases are enough for an AI agency demo?
There is no universal number. The set should cover each important class of work and be large enough to expose variation; agree the population and sampling method with the people who own the risk, then report results separately for rare but serious exceptions.
Who should own the prompts and evaluation records?
Your contract should give you continuing access to the prompts, rules, configurations, test definitions, outputs, and logs needed to operate and audit the service. If the agency retains ownership of reusable components, require a license and export format that lets you migrate.
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You can still evaluate the service behavior, but demand version-change notice, performance regression testing, data-use terms, security evidence, and a remedy if a hidden change harms the agreed KPIs. Lack of model disclosure is a portability and change-control risk, not automatic proof that the service is unsuitable.
Quick Recap
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