AI Agents & LLM Applications Engineering

• Few-shot chain-of-thought, self-consistency sampling, and structured output parsing
• Enforcing strict JSON schemas using Pydantic, Instructor, and Outlines
• Anthropic & OpenAI tool-calling semantics, error recovery, and parameter validation

• Hybrid search in pgvector and Qdrant (Dense Vector Embeddings + BM25 Sparse Lexical)
• Building custom MCP Clients and Servers in TypeScript & Python for live SQL and Git access
• Context window management, chunking strategies, and dynamic reranking with Cohere

• ReAct and Plan-and-Solve architectures using LangGraph and CrewAI
• Fine-tuning Small Language Models (Llama-3, Phi-3, Mistral) with QLoRA & Unsloth
• High-throughput deployment using vLLM, TensorRT-LLM, and LangSmith evaluation suites

Q1: What is the Model Context Protocol (MCP) and how does it revolutionize LLM tool calling?

Model Context Protocol (MCP) is an open standard created by Anthropic using JSON-RPC 2.0. Rather than hardcoding custom API wrappers for every database and tool, MCP provides a unified client-server architecture. An AI client can dynamically discover available tools, read file/context resources, and execute functions across interchangeable enterprise MCP servers safely.

Q2: How do you prevent infinite loops and token explosion in autonomous ReAct agents?

You implement strict recursion limits (max iteration counters), stateful checkpointing with LangGraph, budget caps on token spend per workflow, deterministic stop conditions, and human-in-the-loop (HITL) pause mechanisms for irreversible tool actions.