I build autonomous AI agents that actually do the work. Real tool use, real integrations, real results. Claude, OpenAI, MCP protocol, LangGraph — the full agentic stack.
Not chatbots. Not prompt engineering demos. Real AI agents that take actions — read emails, update databases, run code, make decisions — with the right guardrails.
Multi-step reasoning agents that plan, execute, verify, and self-correct. Using Claude's tool use, OpenAI function calling, or custom orchestration.
Model Context Protocol expertise. Connect your AI to real tools — Gmail, Slack, databases, APIs. Anthropic's standard, I'm one of the early adopters.
Give your AI agents real capabilities. API calls, file operations, database queries, web scraping. Built with proper error handling and retries.
Retrieval-Augmented Generation done right. Vector databases, semantic search, long-term memory. Your agent actually learns from your data.
LangGraph workflows, state machines, multi-agent systems. Complex business processes running autonomously with AI in the loop.
Agents with boundaries. Approval workflows, dry-run modes, comprehensive logging. Your AI won't do something stupid without you knowing.
Production examples where I've deployed agentic AI for real outcomes. Not toys.
Multi-turn nutrition coaching agent integrated into a live mobile app. Understands context, tracks goals, gives personalized advice. Powered by Claude + custom tools.
See it live →Turkish-speaking nutrition agent with access to a 1,400+ food database. Photo recognition + reasoning + personalized guidance. Live with real users.
See it live →I've been building with Claude, GPT-4+, and agentic frameworks since they emerged. MCP protocol, tool use, function calling — not new concepts to me.
6+ years in data science and ML before agentic AI exploded. I understand the math, not just the API calls. That matters for complex use cases.
I don't ship prototypes and disappear. I build things that run in production with real users. Monitoring, error handling, cost optimization — all covered.
Agentic AI is powerful but has real limits. I'll tell you when it's the right solution and when it's not. No AI-hype salesperson here.
I've built production systems using Claude (Anthropic), OpenAI GPT-4+, and Google Gemini with tool use. I've implemented MCP protocol integrations, LangGraph workflows, RAG systems with vector databases, and multi-agent orchestration. I was an early adopter of these technologies.
Model Context Protocol (MCP) is Anthropic's open standard for connecting AI models to external tools and data sources. It's rapidly becoming the industry standard for agentic AI. If you want your AI to actually do things (not just chat), MCP is the modern way. I've built MCP servers and integrations.
Highly variable. Simple chatbot with a few tools: $3,000-8,000. Production agent system with RAG and multiple integrations: $15,000-50,000. Enterprise-grade multi-agent systems: $50,000+. Includes ongoing optimization. Free consultation to scope it.
Yes. I integrate AI into your existing systems — databases (SQL, NoSQL, vector), APIs, messaging (Slack, WhatsApp), CRMs, custom tools. Your AI becomes part of your workflow, not a disconnected chatbot.
It can be, with proper guardrails. I implement approval workflows, dry-run modes, comprehensive logging, cost limits, and fallback mechanisms. The key is treating AI like any production system — with monitoring, testing, and graceful failure handling.
Free consultation. Tell me what you want to automate, I'll tell you if agentic AI is the right solution and design an approach that actually works.
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