AI Agents

Custom AI agents that do the work - not just chat.

A chatbot answers a question. An AI agent gets a job done: it plans the steps, pulls the data it needs, reasons through the problem, calls your tools, and checks its own work. I build custom AI agents and multi-agent systems for real business workflows - shipped to production, connected to your systems, with a human in the loop where it matters.

What it is

An AI agent is a system that can take a goal and work toward it across multiple steps - deciding what to do next, using tools and data along the way, and correcting itself when something goes wrong. That is the difference between a demo chatbot and something that actually handles work.

For bigger jobs I build multi-agent systems: several specialized agents that hand off to each other - one retrieves, one reasons, one drafts, one verifies. Each is scoped, testable, and observable, so the whole thing is reliable enough to run in production.

Agents are the engine behind self-serve internal tools, support that resolves instead of deflects, research and reporting that runs itself, and any workflow that today eats hours of manual steps.

What you get

Built for production, not a demo

Agents that use your tools

They call your APIs, databases, and services - so they actually take actions, not just talk about them.

Multi-step reasoning

Plan, retrieve, reason, and act across a whole task - with self-correction when a step fails.

Human in the loop

Approval gates and guardrails where decisions matter, so the agent assists rather than runs unchecked.

Grounded, not guessing

Agents pull from your real data and cite it, instead of making things up - reliability you can trust in production.

Observable & testable

Every step is logged and evaluable, so you can see what the agent did and why - and catch regressions.

Built to integrate

Slack, Teams, your web app, or an API - the agent lives where your team already works.

How it works

From first call to production

01

Scope the workflow

We map the exact job the agent should do, the tools it needs, and where a human should stay in the loop.

02

Build & test

I build the agent (or agents), wire in your tools and data, and test against real cases - not happy-path demos.

03

Integrate & ship

Deploy into Slack, Teams, your app, or an API, with monitoring and guardrails around it.

04

Handover

Your team gets the runbooks and observability to operate and extend the agent without me.

Proof

Real systems, shipped

Live products I designed and built end to end - the clearest signal I can build yours.

See full case studies

FAQ

Questions clients ask

What is the difference between an AI agent and a chatbot?

A chatbot responds to a message. An agent pursues a goal: it plans steps, uses tools and data, takes actions, and self-corrects. If you need something that does work - not just answers questions - you want an agent.

How much does AI agent development cost?

It depends on the workflow, how many tools it touches, and reliability requirements. Rather than a made-up figure, I scope it on a call and send a fixed quote - most agent projects start with a short discovery to define exactly what it should do.

Can the agent connect to our existing tools and data?

Yes - that is the point. Agents call your APIs, databases, and services (Slack, Teams, CRMs, internal systems). We pick the integrations that matter for the workflow and keep scope tight.

How do you keep an agent from going off the rails?

Guardrails, approval gates for high-stakes actions, grounding in your real data with citations, and full logging of every step. The agent assists with a human in the loop where it counts, and every action is observable.

How long does it take to build one?

A focused single-agent workflow can ship in a few weeks; a larger multi-agent system takes longer. We scope the timeline and a fixed quote on the first call.

Related

Next Step

Ready to scope this?

Book a 60-minute strategy call. No sales pitch - just honest, actionable direction on your project.

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