I design and ship custom AI agents that do more than chat - they retrieve the right context, reason through multi-step tasks, call real tools, and complete operational work end to end. The goal is never a demo; it is an agent your team can trust in production.
Every agent is engineered around a clear job-to-be-done, a tight tool surface, and guardrails that keep it predictable. That is how an AI agent moves from "impressive" to "dependable".
- A custom AI agent is an LLM wired to your tools, data, and guardrails - it takes action, not just answers questions.
- Reliability comes from the harness around the model: a tight tool surface, grounded memory, a control loop, and evals.
- Agents are built model-agnostically across the Claude, OpenAI, and Gemini SDKs.
- Build an agent when a task needs judgment across multiple steps; use plain automation when the steps are fixed.
Tool-using agents
Agents wired to your APIs, databases, and SaaS tools so they can take action - create records, trigger workflows, send messages - not just answer questions.
Context & memory
Retrieval over your knowledge base plus short- and long-term memory, so the agent stays grounded in your data and remembers what matters across a session.
Multi-step reasoning
Planning, tool selection, and self-correction loops that let an agent break a goal into steps and recover when a step fails.
Guardrails & evals
Input validation, output schemas, permission scoping, and evaluation harnesses so behavior is measured and safe before it reaches users.
What a production AI agent actually needs
A reliable agent is a system, not a prompt. It needs a well-defined task boundary, a curated set of tools, a retrieval layer for grounding, structured outputs the rest of your stack can consume, and an evaluation loop that catches regressions before your users do.
I build each of these layers explicitly. The model is one component; the harness around it - routing, memory, tool execution, validation, observability - is what makes the agent dependable.
How I build them
We start by mapping the workflow the agent will own and the exact tools it needs. I then build the smallest agent that completes that loop, instrument it with evals and tracing, and harden it against the failure modes that surface in real use.
Agents ship behind clear interfaces - an API, a chat surface, or an automation trigger - so they slot into your product and operations without a rebuild.
What is a custom AI agent?
A custom AI agent is an LLM-powered system built for a specific job in your business. Unlike a generic chatbot, it can retrieve your data, use your tools, take multi-step actions, and operate within guardrails you define.
How is an AI agent different from a chatbot?
A chatbot answers questions. An agent acts - it plans, selects and calls tools, updates systems, and completes tasks. The difference is the harness of tools, memory, and control logic wrapped around the model.
Which models and frameworks do you use?
I work model-agnostically across the Claude, OpenAI, and Gemini SDKs, with orchestration via frameworks like LangGraph and tool access via MCP, choosing the stack that fits your latency, cost, and accuracy needs.
Ready to build with AI?
Tell me what you are building. I will map the fastest path from idea to a system you can trust in production.
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