01
I write the agent, you talk to me
No account managers, no handoffs, no telephone game between you and the code. As your AI agent developer I design and write the agent directly — for a focused build that is usually faster and sharper than a team.
AI agent development services
Real agent systems. Not another demo.
Most AI agents work in a demo and fall over in production. I build the other kind: custom AI agents that plan, call your tools, verify their own work before it touches your codebase, and recover when something breaks. Below is exactly how that gets built and shipped — phase by phase.
What you get
Four things every build ships with.
Custom agents
Agents built for your workflow
A custom AI agent that plans a multi-step task, executes it against your real systems, and decides what to do next — written for your problem, not configured from a template.
Tool & MCP wiring
The layer that lets it act
The integration layer that lets an agent touch your APIs, data, and internal tools safely — including MCP servers — so it can do real work in your stack without you adopting anyone else’s framework.
Eval & verification
Proof it works, not vibes
Evals and self-verification wired in from the start: the agent checks its own output, an adversarial pass confirms the result, and irreversible actions wait at an approval gate before they run.
Deployment
Run it unattended
Deployment, observability, logging, and recovery paths — the engineering that takes an agent from a working demo to something you can actually depend on running on its own.
How the work ships
Scope, build, verify, ship.
The same four-phase loop runs on every engagement — and on Agent AFK, the runtime I built it on. Verification before anything reaches your codebase, recovery before it runs unattended.
- 01
Phase 01
Scope a real first build
A short call, then one concrete deliverable — a working agent slice you can run, not a deck. We name the task the agent owns, the tools it may touch, and what "done" means in plain terms before any code is written.
Output: a written scope with explicit success criteria.
- 02
Phase 02
Build in reviewable increments
The agent ships in small, auditable steps. You talk to the engineer writing it, not a project manager, and watch each capability land — plan, tool calls, recovery paths — instead of waiting for a single reveal.
Output: incremental commits you can read and run.
- 03
Phase 03
Verify before it touches anything
Every change is checked adversarially before it reaches your codebase or your data. The agent verifies its own output, an independent pass re-derives the claims, and anything irreversible pauses at an approval gate. This is the same verification loop Agent AFK runs on its own work.
Output: an audit trace + a verification pass on every step.
- 04
Phase 04
Ship with recovery built in
The agent reaches one explicit terminal state — done, blocked, or asking — never a silent half-finish. When an upstream API or model shifts, there is a defined recovery path and a readable log, so you can run it unattended and still know exactly what happened.
Output: production deploy, logging, and documented rollback.
How I build
The principles behind the code.
02
Verification is part of the build
Not a phase at the end. The agent checks its own output and an adversarial pass re-derives the result before anything reaches your codebase. Confidence is a trigger to verify, never a substitute for it.
03
Autonomy, bounded by reversibility
The agent acts freely on reversible steps and pauses for approval on anything it cannot take back — deletes, payments, messages to real people. Aggressive where it is safe, cautious where it counts.
04
Honest about what an agent can do
If an agent is the wrong tool for your problem, I will say so on the first call. No "AI will transform your business" pitch — just a straight read on whether this is worth building.
Proof, not promises
Agents I've actually shipped.
No invented case studies or borrowed logos. These are real builds — see the portfolio for the rest.
Open-source runtime
Agent AFK
A self-hosted runtime for autonomous agents, written from scratch on Anthropic’s raw Messages API — session lifecycle, tool dispatch, sub-agent orchestration, transitive cancellation, and persistent state. It is the harness every engagement runs on, and the proof the build process below is real.
Orchestration framework
Skill & sub-agent pipelines
An open-source orchestration layer for Claude Code: slash-commands wired to real pipelines that spec, build, verify, and ship work, forking parallel sub-agents and gating new capabilities behind evals before they run.
Tool integrations
Agents wired to real tools
Custom integrations connecting agents to live systems — Cursor, E2B, ElevenLabs, Twilio, Smartlead and more — so an agent can take real actions in a real stack instead of demoing in a sandbox.
Questions
Straight answers.
What do your AI agent development services actually deliver?
Designing and building software agents that complete multi-step tasks on their own — planning, calling your tools and APIs, checking their own work, and recovering from errors. I build the whole thing: the agent logic, the tool and MCP integrations, the evals, and the orchestration that ties it together, then harden it so a custom AI agent runs reliably in production.
Are you an AI agent development company or a solo developer?
A solo developer, on purpose. You work directly with the AI agent developer designing and writing the agent — no account managers, no handoffs. For a focused custom build that is usually faster and sharper than routing the work through a team, and you always know who wrote the code you are running.
What does your build process look like, step by step?
Four phases: scope a concrete first deliverable with explicit success criteria, build in small reviewable increments you can run, verify every change adversarially before it touches your codebase, then ship with logging and recovery built in. It is the same verification loop my open-source runtime, Agent AFK, runs on its own work — so the method is proven, not promised.
How do you make sure a custom AI agent is reliable, not just a demo?
Verification and recovery are engineered in from the first commit. The agent verifies its own output, an independent pass confirms it, and anything irreversible pauses at an approval gate. It reaches one explicit terminal state — done, blocked, or asking — never a silent half-finish, and every action lands in a readable audit trace.
Can you build the agent inside our existing stack?
Yes. Most engagements plug into a product you already have. I build the agent layer, the tool and MCP integrations, and the orchestration around your existing systems rather than asking you to migrate onto a platform you do not control.
What does it cost to hire an AI agent developer for a custom build?
Project-based for a defined build, or a monthly rate for ongoing development and iteration. Once the scope and success criteria are clear I give you a fixed quote. Book a call, describe the problem, and I will tell you honestly whether an agent is the right tool for it.
Related services
Where this usually goes next.
A built agent rarely stands alone. Most projects also need its tools wired up, a recurring workflow automated, or a team rolled onto agents in a fixed sprint.
Get started
Have an agent to build?
If you're past “we should use agents” and need an AI agent developer who has actually shipped them to production, let's talk. Bring the problem; I'll tell you whether an agent is the right tool and scope a first build.