AI agent development services for agents that take real actions
I build AI agents that do work rather than just chat: they call tools, read and update your systems, and hand over to a person when they should. Every agent I ship has prompt guardrails, validated structured outputs and evals, because an agent that acts on a wrong answer is worse than no agent at all.
- Claude Certified Developer, 896/1000
- Claude deal coach inside OnOffDeals
- LeadPilot built in 48 hours
What an AI agent does that a chatbot does not
A chatbot answers. An agent decides what to do next and does it through tools you control: look up a record, check a calendar, score a lead, update a CRM, call an internal API. I give each tool a narrow job and a strict input schema, give the model only the tools a task needs, and keep a human in the loop for anything costly or irreversible.
Agents I have built
Most of my agent work sits inside real products and client systems rather than demos. The common thread: each agent works through tools with defined inputs, its output is checked before anything downstream relies on it, and there is a clear point where a person takes over. Here is what that looks like.
- OnOffDeals: a Claude deal coach with prompt guardrails, validated structured output and post-deploy red-teaming
- A custom MCP server exposing structured operations to LLM clients, schema-validated at the boundary
- Hindi/Hinglish voice agents and outbound outreach in the same suite
- LeadPilot: an AI lead-automation CRM where every lead scores itself on arrival, built in 48 hours for the Zerops Challenge
- Sharp Edge Property Group: an n8n and OpenAI assistant that qualifies leads, books appointments and hands over to a human with full context
Guardrails, validation and evals
Agents fail quietly: a plausible answer that is wrong, a tool called with the wrong ID, an instruction hidden in user input. Tool inputs and model outputs are validated against schemas, off-topic or unsafe requests are refused by design, and destructive actions need confirmation. Every prompt or model change runs against an eval set built from real inputs first, and after launch I red-team the agent the way a hostile user would, as I did for the OnOffDeals coach.
How an AI agent project runs
We start with a free call to pick one job worth automating and the systems it touches. I map that job to tools, build a working version against your real data, and agree the eval cases with you before widening scope. I work end to end, from architecture and integrations to deployment and the tracking that shows whether the agent is doing its job.
Rates from $20/hour
Hourly for open-ended work, fixed price for defined projects. The first call is free and there's no obligation.
Frequently asked questions
What do AI agent development services include?
Designing and building software where a language model chooses and calls tools to finish a task, such as qualifying a lead and booking the meeting. The work covers tool design, prompts, guardrails, integrations, evals and deployment, not just a prompt in a chat box.
Which models and frameworks do you use for AI agents?
Mostly Anthropic Claude through the Claude API, plus OpenAI and Google Gemini where they fit. I use native tool calling and MCP servers, LangChain when it genuinely helps, and n8n for agents that live mostly inside business workflows. The model is chosen per task for quality, latency and cost.
How do you stop an agent from doing the wrong thing?
By limiting what it can do and checking what it says. Each tool has a narrow scope and a validated schema, outputs are structured and verified, risky actions need human approval, and changes are tested against evals before release. No model is perfect, so the design assumes mistakes and contains them.
What does it cost to build an AI agent?
My rates start from $20/hour. For a clearly scoped agent I give a fixed-price quote after the intro call, so you know the cost before work begins. Model and hosting usage is a separate running cost that depends on your volume.