Story
I'm Andrew, a principal AI engineer. I work alongside one mid-market operations team at a time to ship one production AI agent in 90 days, and I write all the code myself. Here's why I built the business this shape.
01
The pattern I kept seeing
The way most enterprise software projects get structured is recognizable from a distance. Consulting works on the strategy. Engineering works on the build. The two functions report to different people, on different timelines, with different incentives. The deliverable that comes out the other end is some version of "what consulting recommended, partially implemented by engineering, six months late, with the parts that mattered most to the business not quite working."
I watched this pattern for fifteen years running a software agency in the e-commerce space. The shape of the failure didn't really change. The technology underneath it did, sometimes dramatically, but the failure mode stayed the same. Consulting and engineering weren't connected to the same problem. The business owner was the only person in the room who could see both sides, and they were almost never the person making the technical decisions.
What that structure produces, almost invariably, is a vendor relationship that compounds. Every model upgrade is a new conversation. Every workflow shift is a new statement of work. The expertise lives somewhere outside the company, on retainer, and the cost of removing it grows quarter by quarter. The original buyer started a transformation initiative; what they actually built was a dependency.
The alternative most companies don't see is shorter and cheaper than the version they're being sold. A fixed-scope engagement with an engineer who writes the code, ships it, hands over the operating protocols, and leaves. The work goes from "we're partnering with a firm on AI" to "we run an agent that does this workflow, and our team owns it." That sentence is the whole product.
That gap between what's normal and what's possible is the gap MavenSolutions fills.
02
Where the experience comes from
Twenty years ago I was one of the engineers who built Magento. It taught me what real production software looks like at scale, what breaks when systems meet real traffic, and what the gap between "demo works" and "production works" actually feels like to ship across.
After that, I spent fifteen years running a software development agency in the e-commerce space. Most of that work was Magento builds for businesses that had outgrown what off-the-shelf could do. That's where I watched the consulting-versus-engineering pattern repeat itself, year after year, across hundreds of projects. By the end of it I had strong opinions about why most of those projects failed, and a clear sense of which kinds of engagements actually worked.
This business is the engagement that works, applied to the technology that finally makes it possible.
03
Why this work, now
For most of my career, the kind of business problem I'm best at solving — workflows with quantifiable cost, owned by an operations leader, that nobody on the inside has time to engineer their way out of — was hard to solve cleanly. The available tools either required a multi-quarter integration project (expensive, fragile, hard to own) or didn't exist at all (the work stayed manual).
AI agents change that math. The same workflow that used to require a six-month integration project can now be a 90-day engineering build, owned by a small operations team afterward, running on infrastructure that doesn't require a vendor relationship to maintain. The window where this is true is open right now, and it's not going to be open forever — eventually large platforms will commoditize the easy versions, and engagement-shaped businesses will move on to the next layer of problem.
While the window is open, this is the work I want to ship. One mid-market operations team at a time. One workflow. Shipped, owned, and observable inside ninety days.
04
Why I work this way
01
Most consulting models are structured to maximize revenue per consultant, which produces the multi-client juggling that erodes attention. I take one engagement at a time because attention is the scarce resource in this work. An agent that ships in 90 days requires unbroken context, and unbroken context across three concurrent clients isn't possible without lying to at least two of them.
02
No subcontractors, no offshore handoff, no "principal-led" team where the principal sells the work and someone else builds it. Agent quality is determined by tool design, prompt iteration, and judgment about when to stop adding nodes. Those decisions don't transfer cleanly from senior engineer to junior implementer. The person who scopes the work has to be the person who ships it.
03
Open-ended engagements drift toward the consultant's incentives, not the client's. Fixed scope forces the hard scoping conversations to happen in week one, where they belong. Fixed timeline means I either ship or I don't. There's no version where the engagement quietly extends and the client realizes six months in that they've spent triple what they planned.
04
This is the central one. The other three are operational. This one is the bet. Most enterprise AI work creates a dependency because that's what most consulting incentives produce. I'm betting there's a market of buyers who would rather own the system than rent the relationship, and that I can build a sustainable business serving them.
This is the only thing I do, full-time, by design. A focused service business is the right shape for the work I want to ship, and the math on doing it well outperforms the math on doing it alongside anything else.
05
How I think about agent work
The credibility I care about isn't a list of credentials. It's whether the thinking holds up. Four things I believe about agent engineering, after enough builds to have strong opinions about them.
Skip it and you can't tell whether the agent is improving, regressing, or just lucky on the demo path. Build it first, before any agent code, and the engagement has a spine. The eval set is the contract the agent is being built to satisfy. If the eval set is wrong, the agent will be wrong; if the eval set is right, the agent has a defensible target.
Splitting fusable operations across nodes. Adding state fields nothing reads. Routing tool calls without loop-back edges. Premature abstraction. Each one adds surface area without improving eval scores. The minimum architecture that passes the eval is almost always the right one. Resisting the urge to add capability that doesn't move a metric is where the engineering judgment actually lives.
The demo path works because someone designed the demo. The production path is full of edge cases the demo never touched. A methodology that celebrates pilot completion is celebrating the wrong thing. The signal that matters is whether the agent passes a rigorous eval against real historical data, and whether the team running it can read traces when it fails.
It's the agent's nervous system. Without it, the agent silently rots. Models change, prompts get edited, tool APIs shift, input distributions drift. The agent passes yesterday's evals and fails today's reality, and nobody knows until a customer complains. The instrumentation has to ship before the production traffic does, not after.
Those judgments are what A.G.E.N.T. codifies. The framework isn't a marketing artifact. It's how the work runs.
If you're a mid-market operations leader unsure whether the firms pitching you will leave you with one or the other, that's the conversation this business was built to have.
Forty-five minutes, and you'll know whether to move forward.