AI Strategy
How to Choose an AI Agency: 10 Questions to Ask
Updated Muhammad Taha7 min read
To choose an AI agency, ask questions that reveal how they work, not what they promise. The ten below cover who builds the system, how quality is measured, what happens when the model is wrong, who owns the result, and what it takes to leave. A good agency answers all of them plainly and in writing. Vague or defensive answers are the signal you are looking for.
Key takeaways
- Ask who will actually write the code, and talk to that person before signing.
- An agency that cannot explain how it tests an AI system is guessing about quality.
- Ownership and licensing terms should be settled in the contract, not assumed.
- Model and API usage is an ongoing cost. Know who pays it and how it is capped.
- Be wary of guaranteed results, "fully autonomous" claims and proposals with no success criteria.
Where this guide fits: This guide covers choosing an AI agency anywhere. For a Pakistan-specific checklist read how to choose an AI agency in Pakistan, and for overseas buyers read hiring an AI agency in Pakistan from abroad.
Before you talk to anyone
Write down the process you want to improve, how it works today, roughly how often it happens, and what "better" would look like. One page is enough. Agencies give sharper answers to a concrete problem, and you will be able to compare proposals for the same thing.
The 10 questions
1. Who actually builds it?
Find out whether the people in the sales call are the people who will design and build the system. Some agencies resell work to subcontractors or assemble templates. Neither is automatically bad, but you should know.
Good answer: named engineers, their background, and a chance to speak to them. Red flag: "our team" with no names, or reluctance to put an engineer on a call.
2. How do you evaluate quality?
AI systems are not right or wrong in the way ordinary software is. They are right some share of the time, and that share has to be measured. Ask to see how they test.
Good answer: a test set of real examples from your data, agreed pass criteria, results shared with you, and re-testing after every change. Red flag: "we test it thoroughly" with no method, or quality judged by a demo.
3. What happens when the model is wrong?
It will be, sometimes. The question is whether the design expects that.
Good answer: specifics, such as human approval for risky actions, validation of outputs in code, limits on what the agent may do, a refusal path when it is unsure, logs for every decision, and a way to undo. Red flag: "our system doesn't hallucinate", or no mention of human review anywhere.
4. Who owns the code, prompts and data?
Ownership should be explicit. That includes source code, prompts and configuration, test sets, workflow definitions, and the accounts everything runs in.
Good answer: the terms are clear and in writing, and your data is used only for your project. Red flag: the system lives only in the agency's accounts, or the contract licenses the work to you instead of assigning it, without a clear reason.
5. How are model and API costs handled?
Every task an agent performs uses a paid model or other services. Those costs continue after the build.
Good answer: an estimate of usage per task, who is billed (ideally you, directly, in your own provider account), spending limits and alerts, and design choices that keep usage sensible. Red flag: usage costs not mentioned at all, or bundled into a fee with no breakdown.
6. What are your security and data practices?
The system will touch customer data, internal documents or business systems. Ask how that is protected.
Good answer: least-privilege access with dedicated service accounts, secrets stored properly, clarity on which providers process your data and under what terms, what is logged and for how long, and willingness to sign a confidentiality or data processing agreement. Red flag: asking for admin credentials by email, or no answer on where data goes.
7. What happens after launch?
AI systems need upkeep. Models are updated or retired by their providers, your documents change, and real users find new edge cases.
Good answer: a defined support period, monitoring, a process for fixes and improvements, and a clear statement of what is included and what is extra. Red flag: a handover with no maintenance option, or an open-ended retainer with no defined scope.
8. What is the scope, and how will we know it worked?
A proposal should say what will be built, what is excluded, and how success is judged.
Good answer: a written scope tied to one process, measurable acceptance criteria agreed before work starts, a timeline with milestones, and a list of what they need from you. Red flag: a broad "AI transformation" with no deliverables, or success defined only as delivery of the software.
9. Can you show similar work, a demo or references?
Evidence matters more than claims. Not every agency can share client names, and newer ones have shorter track records, so look at what they can show.
Good answer: a live demo of something they built, a walk-through of the architecture, public technical writing or code, or references you can contact. Red flag: impressive numbers with no way to verify them, or logos with no detail on what was done.
10. What is the exit plan?
You should be able to move the system to your own team or another vendor.
Good answer: documentation, a handover session, standard tools and languages, and export of all data and configuration. Red flag: a proprietary platform only they can operate, with no export.
Quick reference
| Question | What you want to hear | Red flag |
|---|---|---|
| Who builds it | Named engineers you can talk to | Anonymous "team" |
| Quality | Test set and pass criteria | Demo as proof |
| Errors | Approvals, validation, logs | "It doesn't make mistakes" |
| Running costs | Estimate, limits, direct billing | Never mentioned |
| Security | Least privilege, clear data flow | Shared admin logins |
| After launch | Defined support and monitoring | Hand over and disappear |
| Scope | Written acceptance criteria | Vague transformation |
| Evidence | Demo, references, technical work | Unverifiable claims |
| Exit | Docs, handover, export | Lock-in |
General red flags
- Guaranteed outcomes. Nobody can honestly guarantee a specific saving before seeing your process and data.
- Agents for everything. A trustworthy agency will sometimes tell you a simple rule, an existing product or no automation at all is the better answer.
- No questions about your process. If they propose a solution before understanding the work, it is a template.
- "Fully autonomous" as a selling point. For most business processes, human control over risky steps is a feature.
- Pressure to commit to a large programme first. A small, scoped first project is how both sides find out whether the fit is real.
How to compare the answers
Send the same one-page brief and the same ten questions to each agency. Score the answers for specificity, not polish. Then ask for a small paid first phase, such as a scoped plan or a narrow pilot, with its own acceptance criteria. How an agency behaves on a small job tells you most of what you need to know about a large one. For background on what drives the size of a project, see what it costs to build an AI agent.
Related guides
- How to Choose an AI Agency in Pakistan: A Buyer's Checklist
- AI Agency vs Freelancer vs In-House Team: Which Should You Choose?
- Hiring an AI Agency in Pakistan as an International Client: What to Know
- How to Choose an LLM for Your Business: GPT, Claude or Gemini?
- Service: AI Workflow Automation
Frequently asked questions
Should I choose a large agency or a small one?
Size matters less than who does the work and how. Large firms offer breadth and capacity. Small ones often give you direct access to the engineers. Ask the same ten questions of both.
Does the agency need experience in my industry?
It helps but is rarely essential. Experience with your type of process and your systems, such as your ERP or CRM, usually matters more than the industry label.
Is a fixed-scope project better than an hourly arrangement?
For a first project, a fixed scope with clear acceptance criteria protects both sides. Ongoing improvement after launch tends to suit a retainer with a defined set of activities.
Sources and further reading
Primary documentation and research behind this guide.
These questions apply to any agency, including us. Growmerz is an engineer-led AI agency, and you are welcome to ask us all ten. You can read how we approach custom AI agents, or book a free AI audit and put the questions to us directly.