Service
LLM Agent Development — LangGraph, CrewAI & MCP
An LLM agent is a language model that decides what to do next — which tool to call, what to look up, when to hand back to a human — instead of answering in a single pass. I build agents as explicit, inspectable systems: LangGraph or CrewAI for orchestration, MCP servers for tool access, and a human approval step wherever an action is expensive to undo.
Orchestration with LangGraph and CrewAI
LangGraph models an agent as a graph of states and transitions, so every step is explicit and the flow can branch, loop and resume. CrewAI suits work that splits naturally across roles — several specialised agents, each with its own brief, coordinating on one outcome. The choice follows the shape of the problem, not a default.
Tool access through MCP servers
The Model Context Protocol is an open standard for connecting models to tools and data. An MCP server exposes a system — a CRM, a database, an internal API — as a set of typed tools that any MCP-capable model can call, so an integration is written once rather than once per model.
Human-in-the-loop where it matters
Agents that touch money, customers or records route those actions through an approval step before they execute. The agent does the work of preparing the action; a person decides whether it happens.
Stack
Have a system like this in mind?
Based in Lahore, working US Eastern hours (9am–5pm ET).