Building autonomous multi-agent swarms, custom Model Context Protocol (MCP) servers, structured LLM outputs, and full-stack AI applications.
Developing custom MCP servers and clients to securely bridge Claude, OpenAI, and local LLMs directly into proprietary enterprise tools, databases, and local file systems with full protocol specification compliance.
Designing multi-agent frameworks (LangGraph, AutoGen, custom Python SDKs) with dedicated supervisor agents, tool routing, sub-agent spawning, and state persistence.
Enforcing 100% deterministic schema responses using Pydantic models, JSON mode constraints, and strict function calling validation to guarantee error-free code execution.
Engineering low-latency conversational agents combining real-time speech-to-text (Whisper/Deepgram), streaming LLM generation, and text-to-speech synthesis (ElevenLabs, Cartesia).
Building reactive frontends (Next.js, modern CSS, WebSockets) backed by Python FastAPI or Node.js microservices streaming live token outputs and agent status updates.
@mcp.tool()
async def query_enterprise_knowledge(query: str, domain: str) -> dict:
"""Queries vector DB & returns contextual evidence with confidence score."""
results = await hybrid_retriever.search(query=query, filter={"domain": domain})
return {"status": "success", "evidence": results.documents, "score": results.top_score}