How do you vet an AI agency before hiring them?

Four things to evaluate: specificity over breadth (they should have a clear domain focus, not claim to do everything), integration depth (real answers about authentication, error states, rate limiting), ownership structure (builds in your environment on your accounts, no proprietary platform lock-in), and audit-first mentality (proper process mapping before any development begins).

What questions should I ask an AI agency in the first conversation?

Ask: describe a project that didn't go as planned and how you handled it. What won't you automate and why? How do you measure success at 90 days and who owns that measurement? What does the client do if something breaks after the engagement ends? How many clients have expanded with you versus moved on, and why?

What are the red flags when hiring an AI agency?

Non-obvious red flags: long discovery phases before any scoping (often a revenue mechanism), proposals that lead with the AI model rather than the problem being solved, vague delivery timelines with no milestones, resistance to documentation, and answers that lack specificity about past failures or what they won't build.

What should an AI agency engagement structure look like?

A focused, well-scoped AI project should have a defined timeline (typically 2–5 weeks), clear milestones, a defined deliverable, and success metrics agreed at kickoff. If it can't be described precisely, it hasn't been thought through precisely. Start with a small first build to test the agency's execution before committing to a larger engagement.

Should I start with a large AI platform engagement or a small project?

Always start small. A focused first build tells you everything about how an agency executes — how they communicate during the build, whether they surface problems early, whether the deliverable matches the scope. That information is worth more than any proposal document and protects you from expensive mistakes.

How to Hire an AI Agency Without Getting Burned

The AI agency market is flooded with vendors who can demo well but can't deliver. Here's what actually separates agencies that build real systems from agencies that sell slides and disappear.

Hiring an AI agency requires vetting four things most businesses overlook: specificity of domain focus, actual integration depth (not surface API familiarity), ownership structure (builds should live in your environment, not the agency's), and an audit-first approach that maps your process before building. Agencies that pass all four checks are rare — and worth the extra diligence to find.

Why is hiring an AI agency harder than hiring other vendors?

Three years ago, hiring an AI vendor was a niche decision. Today there are hundreds of agencies claiming to build AI systems for business — automation shops, generalist consultants, offshore development teams, and everything in between. Most of them can produce impressive demos. Very few of them can produce a system that runs reliably six months after the kickoff call.

The problem is that the evaluation frameworks businesses use to hire other vendors — case studies, references, proposal quality — don't adequately filter for the specific failure modes that AI projects run into. A bad web design agency delivers a slow website. A bad AI agency delivers a system that fails in production, can't be maintained, or automates the wrong thing entirely.

What do you actually need to evaluate in an AI agency?

Specificity over breadth. Every AI agency today claims to do everything. The ones who can actually deliver are typically deep in a specific domain — workflow automation, sales tooling, document processing — and have repeated the same type of build enough times to know where the problems are. Ask what they build most often and what their last five projects had in common. Vague answers are a red flag.

Integration depth, not just familiarity. Building AI on top of tools you already use — your CRM, your email, your project management system — requires real integration work, not just surface-level API calls. Ask specifically how they handle authentication, error states, rate limiting, and what happens when an integration breaks. Agencies that have done this before have real answers. Agencies that haven't will talk around it.

Ownership structure from day one. Who owns what gets built? Where does it run? What dependencies does it create? A reputable agency builds inside your environment, connected to your accounts. You should be able to part ways with them tomorrow and the system keeps running. If the answer involves proprietary platforms, usage fees, or "we manage the infrastructure," understand exactly what that means before you sign.

The audit-first mentality. Agencies that start building before they fully understand your process will build the wrong thing. The first engagement should include a detailed process audit — not a discovery call, an actual mapping of the workflow, the edge cases, and what success looks like. If a vendor is ready to start development after one call, that's a problem.

What questions are worth asking an AI agency in the first conversation?

Ask them to describe a project that didn't go as planned and how they handled it. Ask what they won't automate and why. Ask how they measure success at 90 days and who owns that measurement. Ask what the client does if something breaks after the engagement ends. Ask how many of their clients have expanded with them versus moved on — and why.

These questions don't have trick answers. They're designed to surface whether the agency has done enough real work to have real opinions.

What are the non-obvious red flags when evaluating AI agencies?

Long discovery phases before any scoping — this is often a revenue mechanism, not a methodology. Proposals that lead with the AI model or platform rather than the problem being solved — the technology should follow the business case, not precede it. Vague delivery timelines with no milestones — "we'll build until it's done" doesn't give you leverage if the project drifts. Resistance to documentation — if they won't write down how it works, you're not the owner of what they built.

What a good engagement structure looks like

A focused, well-scoped AI project — one automation, one agent, one integration — should have a defined timeline (typically 2–5 weeks), clear milestones, a defined deliverable, and success metrics agreed at kickoff. You should know exactly what you're getting, when you're getting it, and how you'll know if it worked. Anything that can't be described that precisely probably hasn't been thought through precisely.

What is the right starting point when hiring an AI agency?

Start with a small, well-scoped project rather than a large platform engagement. A focused first build tells you everything you need to know about whether an agency can execute — how they communicate during the build, whether they surface problems early, whether what they deliver matches what they scoped. That information is worth more than any proposal document.

How to Hire an AI Agency (Without Getting Burned)