An AI agent is software that does a specific job using a language model as one of its parts — it reads your real data, makes a decision or takes an action, and asks for a human when it should. Not a chat window, not a demo: a working piece of your operation.
Everyone has seen the demo where an AI assistant books a flight or summarises a document on the first try. Most of those demos break the moment they meet real data, real edge cases, and a real bill at the end of the month. The gap between "works most of the time in a demo" and "a business can rely on it" is a different project entirely — and that gap is where I work.
What I build usually doesn't look like ChatGPT. It looks like an ordinary tool your team already understands, with AI quietly running specific decisions underneath. It connects to the systems you already use, it does work you can measure, and it survives contact with a normal Tuesday.
What can an AI agent do?
An AI agent is built for one job, named in the terms of your own operation. A few of the shapes this work usually takes:
Handling repetitive intake — Lead qualification, support triage, sorting incoming requests. The agent reads what comes in, decides where it goes, and writes the result back to the tool your team already watches — at a volume a person can't keep up with.
An internal assistant that knows your business — Not a generic chatbot, but an assistant grounded in your own documents, your wiki, your past tickets. It answers questions about how your company actually works, with the right answer drawn from your real material instead of made up.
Processing documents and data — Reading invoices, contracts, applications, or forms; pulling out the fields that matter; enriching records; flagging the ones a human needs to look at. The boring work that quietly eats hours every week.
Taking real actions, not just answering — An agent that updates your CRM, drafts and sends an email, generates a document, or kicks off the next step in a workflow — with the safe actions automatic and the risky ones held for a person to approve.
A product with AI at its core — When you're building something where the model isn't a bolt-on feature but the engine of the thing, and you need an engineer who has shipped AI in production, not just prototyped it.
What makes an AI agent production-grade?
The demo is the easy part. The work that makes an agent something a business can lean on is the unglamorous part — and skipping it is why most AI projects quietly fail.
Testing on your real examples — Before I trust an agent, I check it against a set of your own real cases — good outcomes and bad ones — and measure how often it gets them right. Honest numbers, not a good feeling. The same check runs again on every change, so a fix never silently breaks something else.
A plan for when it's wrong — No AI system is right every time. A production agent has worked-out behaviour for the misses: retry, hand off to a person, try a different approach, or simply refuse to act. "Trust the model and hope" is not a plan.
Cost you can see and cap — Spending limits per task, usage alerts, and hard ceilings from day one, so you never wake up to a surprise bill. The cost of every decision the agent makes is visible, not a mystery.
A way to see what happened — Every step the agent takes is recorded — what it read, what it decided, how long it took, what it cost. When something goes wrong you debug it by reading the record, not by guessing.
What technology powers an AI agent?
The tools matter less than the discipline, but since people ask — here is what sits under the work, each chosen for a plain reason.
The models — Frontier models from OpenAI, Anthropic, and Google for hard reasoning; open models like Llama and Qwen for bulk work or when your data can't leave your servers. Most agents use two or three together: a cheap fast one for the simple sorting, a stronger one for the real work.
The plumbing — Mostly plain code rather than a heavy framework. Frameworks that hide what the model actually sees make problems harder to find; I keep the moving parts visible so the agent is easy to debug and quick to change.
Knowing your documents — When an agent needs to answer from your own material, that material is stored so the right piece can be found and quoted accurately. Getting that retrieval right is usually the difference between a useful assistant and a confident wrong one.
Connecting to your systems — The agent lives inside the tools you already run — your CRM, your helpdesk, your database, scheduled jobs. The AI is one component in a real system, not a separate island.
How does an AI agent project work?
First, a thirty-minute call. You tell me the specific task. I'll tell you honestly where AI genuinely helps and where plain, predictable code would do the job better and cheaper. We build a first set of real examples to measure against. Free, usually this week.
Then a working prototype. I build the first real version and measure it against your examples — showing actual numbers, not vibes. We look at the outputs together on real cases, find what's wrong, and add those failures to the test set so the next round is sharper.
Then the production build. I connect the agent to your real systems, add the cost controls, the record-keeping, and the fallback behaviour for when it's unsure. It's deployed somewhere safe and validated against real traffic before it touches anything that matters.
Then launch, and what comes after. I can stay on as your engineer for prompt updates, new cases, and model upgrades as the agent grows — or hand it over cleanly, with the test set your own team can run, so you're never dependent on me.
Is an AI agent right for you?
A good fit if:
- You have a specific, repetitive task in mind — not a vague wish to "add AI somewhere"
- You understand AI gets things wrong sometimes, and you want someone to engineer around that honestly rather than hand-wave it
- You have real examples to learn from — past cases, records, transcripts — so the agent can be measured against reality
- You want the agent built into the systems your team already uses, not a standalone widget
- You care about accuracy, cost, and owning what gets built — not just the flashiest demo
Not a fit if:
- You want a simple chat widget for your website — that's an off-the-shelf product someone already sells, not a custom build
- You have no real examples and no way to produce any — without something to measure against, an agent is guessing in public
- You expect the agent to be "creative" and "surprising" — my work is about reliability, not novelty
- You expect 100% accuracy — no AI system reaches that, and anyone who promises it is not being straight with you
- Your use case is bulk content generation for search-engine spam — that's not the work I do
Frequently asked questions
Why build a custom AI agent instead of using an existing product?
Custom makes sense when your workflow is specific enough that no off-the-shelf product really fits, or when your data is sensitive enough that you can't send it to someone else's service. For generic needs — writing help, meeting notes, plain summarising — just buy the product. Custom is for the work that is genuinely yours.
How do you handle the AI getting things wrong?
Testing first. I build a set of real examples, measure how often the agent is right, and design clear behaviour for the misses — retry, escalate to a person, try a different approach, or refuse to act. No system is perfect, so the plan for being wrong is part of the build, not an afterthought.
What about the AI making things up?
For factual work — answering from your documents, pulling data from forms — I ground the agent in your real material so its answers are drawn from something true and can be traced back to the source. The testing tells us honestly how often it gets it right before anyone relies on it.
Can the agent take actions on its own?
Yes, with guardrails. I usually design it in tiers: read-only actions happen automatically, actions that change something are staged for review, and high-stakes actions always wait for a person to approve. Where that line sits depends on your task and how much risk you're comfortable with.
What ongoing work does an agent need after launch?
Some. Prompts need updating as models change, new examples get added to the test set, and new edge cases turn up as real use grows. I can stay on as a retained engineer for that, or hand the agent over with the test set your team can run themselves — your call.
Will you sign an NDA?
Yes. A standard mutual NDA before anything sensitive is discussed — no problem at all.
Let's talk
Bring a specific task — a repetitive job you'd like handled, a decision you'd like AI to make, an assistant you wish your team had. We'll work through whether AI is genuinely the right answer and what it would take to build. A thirty-minute discovery call is free — no deck, no sales, just a real conversation.