Most AI agent demos look impressive. Then you try to put them in front of real users and they hallucinate, loop forever, or cost $40 per conversation. I build the ones that don't.

I design and ship production AI agent systems for startups and businesses: autonomous workflows, tool-calling agents, RAG pipelines, multi-agent orchestration, and conversational interfaces that handle real-world edge cases without falling apart.

Framework-agnostic. I pick the right tool for the job, not the one I saw in a tutorial. That means LangChain and LangGraph when graph-based orchestration makes sense. Anthropic's tool use and Claude API when you need structured outputs and reliability. OpenAI function calling when the ecosystem fits. CrewAI or custom orchestration when off-the-shelf frameworks add more complexity than they solve. The architecture serves your problem, not the other way around.

AI-powered research tools that verify claims against live web sources (Claude API, structured outputs)

Conversational agents with memory, tool access, and multi-step reasoning

RAG pipelines over proprietary document sets with vector search (Pinecone, pgvector, FAISS)

Multi-agent systems with role isolation and handoff logic

Internal automation agents that replaced manual workflows and saved teams 20+ hours/week

How I work: I start with your use case, not a framework. We define what the agent needs to do, what data it needs access to, what "good enough" looks like, and what failure modes matter. Then I build iteratively: working prototype in days, production-hardened system within 2-3 weeks. You'll see it working before you've spent half the budget.

Daily async updates. Staging environments you can test yourself. No black-box handoffs.

Who this is for: Founders building AI-native products. Teams automating complex internal workflows. Businesses that tried ChatGPT wrappers and need something that actually handles their specific data and logic. If your AI project needs to work reliably at scale, not just demo well, we should talk.