What Should I Look for in an Agency That Builds Custom AI Workflows for Mid-Size or Enterprise Companies?

You've decided your company needs custom AI workflows — not off-the-shelf chatbots or bolt-on copilots, but real automation woven into your business processes. Good. Now comes the harder question: who do you hire to build them?

The market is flooded with agencies that added "AI" to their website six months ago. According to a 2024 RAND Corporation study, more than 80% of AI projects fail — twice the rate of non-AI IT projects. Gartner estimates that only 48% of AI projects make it into production, with at least 30% of generative AI projects abandoned after proof of concept by the end of 2025.

The agency you choose will be the single biggest factor in whether you land in the success column or burn budget on a demo that never ships. Here's what to look for — and what to run from.

1. They Build for Production, Not Demos

Every agency can build a compelling AI demo. The question is whether they've shipped AI systems that run unattended, at scale, in production environments with real data and real users.

Ask specifically:

How many AI workflows are you running in production right now? Not "have built" — are running. Active, monitored, maintained.
What's your average time from proof of concept to production? If they can't answer this, they haven't done it enough to track it.
Walk me through a failure. Any agency with real production experience has war stories. If they don't, they're either lying or too new.

The RAND study found that one of the top root causes of AI failure is that organizations focus more on using the latest technology than on solving real problems for their intended users. A good agency will push back on flashy approaches and ask what business outcome you're actually trying to achieve.

2. They Understand Your Data — and Tell You the Truth About It

Data quality is the number one killer of AI projects. A Gartner survey of 248 data management leaders found that 63% of organizations either don't have — or aren't sure they have — the right data management practices for AI.

The right agency will audit your data situation early and give you an honest assessment. They'll tell you:

What data you have that's usable as-is
What needs cleaning, structuring, or enrichment before AI can touch it
What data you're missing entirely and how to start collecting it
Whether your current data infrastructure can support AI workloads

If an agency says "we can work with whatever you have" without looking at your data first, that's a red flag. The RAND study identified lacking the necessary data to adequately train an effective AI model as one of the five root causes of project failure.

3. They Architect for Flexibility, Not Lock-In

The AI landscape is moving faster than any technology wave in history. The model you use today may be obsolete in six months. The framework you build on may be superseded by something dramatically better next quarter.

As the World Economic Forum notes, composable AI systems protect organizations from lock-in, accelerate experimentation, and ensure agility — allowing companies to augment existing tech stacks rather than rebuild them.

A good AI agency will:

Abstract the model layer. Your workflows shouldn't break if you swap Claude for GPT or switch to a fine-tuned open-source model. The agency should build an abstraction layer that makes model changes a configuration change, not a rewrite.
Own your code and data. You should have full access to source code, deployment pipelines, and all training data. If the agency disappears tomorrow, you can keep running.
Use standard infrastructure. Kubernetes, standard cloud services, well-documented APIs — not proprietary platforms that only the agency understands.
Design for composability. Each AI workflow should be a modular component that can be recombined, replaced, or extended without touching the rest of the system.

4. They Have a Clear Point of View on AI Architecture

Beware the agency that says "we're tool-agnostic" about everything. That's usually code for "we haven't built enough to have strong opinions."

An experienced AI agency should have opinionated answers to questions like:

When do you use agents vs. simple chains vs. traditional automation?
How do you handle prompt versioning and regression testing?
What's your approach to monitoring AI systems in production?
How do you manage cost at scale — token budgets, caching strategies, model routing?
What's your guardrail strategy for preventing hallucinations from reaching end users or corrupting data?

If they can't walk you through their architecture patterns with specifics, they're still figuring it out on their clients' dime.

5. They Scope Aggressively Small, Then Expand

Enterprise AI projects fail when they try to boil the ocean. McKinsey has documented that the primary failure mode for gen AI programs is the inability to cross the chasm from prototype to production — driven by risk concerns, cost overruns, and scope that expands faster than value is delivered.

The right agency will:

Start with one workflow. Not ten. Not an "AI strategy roadmap" that spans 18 months. One concrete workflow that delivers measurable value in weeks.
Define success metrics before writing code. "We'll save your team 10 hours per week on invoice processing" is a good scope. "We'll transform your operations with AI" is a red flag.
Ship in weeks, not months. If the first working version isn't in front of real users within 4-6 weeks, the engagement is too big or the team is too slow.
Expand based on evidence. Once workflow #1 proves its value, use what you learned to pick workflow #2. The data, the edge cases, and the organizational readiness you discover in round one should directly inform round two.

6. They Know the Difference Between AI and Software Engineering

A custom AI workflow is 20% AI and 80% software engineering. The model call is the easy part. The hard parts are:

Integrating with your existing systems (CRM, ERP, content management, databases)
Handling errors, retries, and edge cases gracefully
Building monitoring and observability so you know when something goes wrong
Managing authentication, authorization, and audit trails
Designing UIs that make AI outputs useful to the humans who need them

This is why pure "AI shops" with no software engineering depth often struggle with enterprise work. They can build the model layer but can't wire it into the messy reality of your tech stack. Look for an agency that has deep software engineering foundations — ideally one that was building complex systems before the AI wave and added AI as a capability, not the other way around.

7. They Take Security and Compliance Seriously

For mid-size and enterprise companies, AI workflows inevitably touch sensitive data — customer records, financial information, proprietary business logic. The agency you hire needs to demonstrate:

Data handling policies. Where does your data go? Which models see it? Is it used for training? A good agency will have clear answers and contractual commitments.
Compliance awareness. They should understand the regulatory landscape relevant to your industry — HIPAA, SOC 2, GDPR, or whatever applies — and build accordingly.
Audit trails. Every AI decision that affects customers, finances, or operations should be logged and traceable. This isn't optional for enterprise.
Human-in-the-loop design. For high-stakes decisions, the system should escalate to humans rather than act autonomously. The agency should build this in from day one, not bolt it on later.

8. They Have a Real Team, Not a Prompt Engineer and a Prayer

Building production AI workflows for enterprise requires a cross-functional team:

AI/ML engineers who understand model selection, fine-tuning, prompt engineering, and evaluation
Backend engineers who can build reliable, scalable integrations
DevOps/infrastructure expertise for deployment, monitoring, and scaling
A technical lead who can translate between your business stakeholders and the engineering team

Ask to meet the team that will actually do the work — not just the sales team. Ask about their experience with similar projects. And be wary of agencies that outsource the core AI work to subcontractors.

The Questions That Separate Pretenders from Practitioners

When you're evaluating agencies, these questions will quickly separate the ones who've done real work from the ones running on hype:

"Show me an AI workflow you built that's been running in production for more than 6 months. What broke?" — Production experience can't be faked.
"If we need to switch from OpenAI to Anthropic next year, what does that migration look like in your architecture?" — Tests whether they've built for flexibility.
"What's your approach when the AI gives a wrong answer that reaches a customer?" — Tests whether they've thought about failure modes.
"Walk me through how you'd scope a project for us — what do the first two weeks look like?" — Tests whether they start with discovery or jump straight to building.
"What AI project have you turned down, and why?" — The best agencies know when AI isn't the right solution.

Key Takeaways

Production track record matters most. Demos are easy; running AI reliably in production is hard. Prioritize agencies with live, maintained systems.
Data honesty is non-negotiable. The right agency will audit your data and tell you the truth, even if it means a longer timeline.
Flexibility over lock-in. Composable, model-agnostic architecture protects you as the AI landscape shifts.
Software engineering depth is the real differentiator. The model call is the easy part; integrating it into your business is the hard part.
Start small and prove value. Agencies that want to start with a massive roadmap are optimizing for their revenue, not your results.
Security and compliance from day one. Enterprise AI without audit trails and data governance is a liability, not an asset.

At Last Rev, we've built custom AI workflows that run in production every day — not as demos, but as core business infrastructure. Our background in composable architecture and enterprise software engineering means we don't just build the AI layer; we build the entire system around it. If you're evaluating agencies, we'd be happy to talk — even if you end up going with someone else.