How to Evaluate AI Development Agencies: A Founder's Guide

The number of AI development agencies that appeared between 2023 and 2026 is staggering. Many of them are good. A meaningful portion are consultancies, marketing firms, or individual freelancers who added "AI" to their positioning after GPT-4 launched. When you're spending $15,000–$80,000 on an AI build, knowing how to evaluate AI agency options before you sign is worth the time.

This guide covers the eight questions we'd ask if we were on the other side of the table — and the answers that should raise flags.

Why Evaluation Matters More in AI Than Other Engineering

AI agency quality is harder to assess than traditional software agencies for a structural reason: the outputs are harder to evaluate before you've received them.

A traditional software agency can show you a live product. You can inspect the codebase, count the features, test the functionality. An AI system's quality is partly in the model configuration, prompt architecture, evaluation framework, and production monitoring — none of which are visible from a demo.

This means you need to evaluate the process, not just the portfolio.

Question 1: What AI Models Do You Actually Use, and Why?

The right answer is specific and justified. "We use Claude 3.5 Sonnet for agentic tasks because it outperforms GPT-4o on SWE-bench and has a larger context window for multi-file reasoning" is a good answer. "We use the latest models" or "we use OpenAI" is not.

An agency that can't explain their model selection criteria hasn't done enough production AI work to have formed real opinions. This question costs nothing to ask and immediately distinguishes agencies that are practitioners from those that are positioners.

Question 2: Can I See the Evaluation Framework for a Previous Project?

Shipping an AI product without an evaluation framework is the equivalent of shipping software without tests. You might get lucky, but you have no way to know when it breaks or why.

Ask to see an example of how they measured whether an AI system was working correctly before launch. What metrics did they track? What failure modes did they anticipate? How did they handle hallucination in production?

If the agency built an AI-powered document processing tool, you want to see something like: precision/recall targets, examples of edge cases they tested, and the monitoring they set up post-launch. If they look confused by the question, move on.

Question 3: Who Actually Does the Work?

AI agency economics are often opaque. Some agencies sell AI consulting and deliver it with offshore labor at thin margins. Some have a small senior team that handles scoping and hand off execution to juniors. Some are genuinely end-to-end.

Ask directly: who will be the primary engineer on my project, what's their background, and can I talk to them before signing?

A good agency has no reason to avoid this. If they're protective about letting you talk to the people who will build your product, that's a risk signal.

Question 4: How Do You Handle a Project That Isn't Working?

Every AI project hits a point where the initial approach doesn't perform as expected. The model hallucinates more than tolerated. The latency is too high. The user interface doesn't match how users actually interact with the AI feature.

How an agency responds to this inflection point is what separates a partner from a vendor. Ask: "Tell me about a project where the initial approach didn't work. What happened?"

Listen for honesty about failure, a structured process for diagnosing problems, and evidence that the client stayed informed throughout. An agency that can only tell success stories either hasn't done enough hard work or isn't being honest.

Question 5: What Do You Deliver at the End?

This should be clearly defined in the contract, but the conversation before signing reveals a lot.

Good deliverables for an AI engagement:

Working, tested code in a repository you own
Documentation of the AI architecture (model selection, prompt structure, evaluation criteria)
A handoff session where the agency walks your team through the system
Monitoring setup so you know when something breaks post-launch

Weak deliverables:

"A working product" with no specification of what "working" means
No documentation of the AI configuration
API keys and a demo, with the underlying system remaining opaque

If you can't maintain and modify the system after the engagement ends, you've rented a product rather than built one.

Question 6: What's Your Pricing Model and What's Not Included?

AI project pricing has more variables than traditional software. Token costs, third-party API fees, cloud infrastructure, and model fine-tuning costs can add up materially above the agency fee.

Ask for a clear breakdown of:

What's included in the agency fee (design, dev, QA, deployment, documentation)
What third-party costs you'll bear directly (OpenAI/Anthropic API, hosting, monitoring tools)
What happens if the scope changes mid-project

A fixed-scope, fixed-price model is cleaner and protects both parties. Time-and-materials models can be legitimate but require more trust and more active project management on your side.

Question 7: Can I Talk to a Reference at My Stage?

References are standard practice and any credible agency expects this. Specifically, ask for references from:

Companies at a similar stage (seed, Series A, SME — whatever matches you)
Projects with similar technical complexity
Engagements that completed at least 6 months ago (so the reference can speak to post-launch performance)

The questions to ask the reference: Did they deliver on time? Did the system work as specified? Did the AI feature perform as expected in production, or did you have to rebuild it? Would you use them again?

Question 8: What Happens If I Need to Change Something After Launch?

AI systems require ongoing maintenance in ways that traditional software often doesn't:

Models get updated and behavior changes
New failure modes emerge from real user inputs you didn't anticipate
Your product requirements evolve

Ask whether the agency offers a maintenance retainer, what they charge for post-launch changes, and whether they'll train your team to make small adjustments independently.

An agency that sets you up to maintain and evolve the system yourself (or offers a fair retainer for ongoing support) is behaving like a partner. An agency that builds in dependencies that require them to be involved in every future change is optimizing for recurring revenue, not your outcomes.

Red Flags to Watch For

No examples of production AI systems (demos only, no live references)
Vague about model selection ("we use the best models")
Unwilling to connect you with the actual engineers before signing
No discussion of evaluation or testing methodology
Pricing that doesn't account for API costs (this often surfaces later as an unexpected expense)
Long initial timelines — if an agency says 6 months for an AI MVP, that's misaligned with how AI product development works in 2026

How We Work at 100x Engineering

We're an AI development agency, so take this with appropriate skepticism — but we'll tell you how we answer each of these questions ourselves:

We use Claude 3.5 Sonnet and GPT-4o, routed by task type; we can explain why for any given project
We include evaluation frameworks in every engagement, defined at scoping
You'll talk to the engineers before signing, because they do the scoping
Our base engagement (21-day sprint) has a fixed scope and a fixed price — see the MVP sprint guide for what's included
We do a structured handoff, including documentation and a 60-minute walkthrough

We've also written an honest comparison of AI agency vs. in-house development that covers when hiring makes more sense than engaging an agency. If you're at the stage where that question matters, read it before you call us.

If you'd like to run us through these questions, book a 15-minute scope call. No pressure, no sales deck — just an honest conversation about whether the sprint model fits your situation.

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How to Evaluate AI Development Agencies