Hedi Manai AI Agent Builder.
Production AI systems that handle real workloads — not demos. Multi-agent architectures built with LangGraph, CrewAI, and MCP, plus open-source tools and technical lessons grounded in real engineering.
About
Engineering systems that run in the real world.
With a background spanning data science, machine learning, R&D leadership, and backend engineering, I bridge the gap between AI research and production reality. I architect autonomous multi-agent systems and agentic AI workflows that solve complex tasks through intelligent orchestration and coordination.
Using the Model Context Protocol (MCP) and frameworks like LangGraph and CrewAI, I build stateful, resilient systems that move beyond prototypes — from workflow automation and data pipelines to full production deployments, all running on scalable Python and FastAPI infrastructure.
Technical Stack & Expertise
The Agent Engineering Playbook
Mastering the art of agentic systems.
A structured curriculum for engineers building real AI systems. The opening series covers production prompt engineering end to end; LangGraph, CrewAI, and MCP deep dives are next on the roadmap. What works, what breaks, and why — no fluff, no theory for its own sake.
What Is Prompt Engineering? A First Principles Introduction
A rigorous first-principles introduction to how prompts work, why they fail, and the core techniques that matter in practice.
Building the Production System Prompt
Master the five-component anatomy and decision protocols required to build reliable system prompts for production agents.
Chain-of-Thought and Self-Consistency
The technique that changed how we think about AI reasoning. Explore the mechanism, seven variants, and self-consistency sampling.
Prompt Chaining and Pipeline Design
Decompose complex tasks into chains of focused prompts. Learn architecture choices, state management, and error handling.
RAG and Context Injection
Master retrieval-augmented generation, chunking strategies, and embedding mechanics to build context-aware AI systems.
Evaluation and Testing
The discipline that turns prompt engineering into engineering. Build eval datasets, choose metrics, and run regression tests.
Projects & Skills
Building AI that actually works.
Open-source tools, agent skills, and the systems I build and maintain. Not experiments — functional software designed to run in the real world, with more shipping over time.
ToolOps: The Service Mesh for AI Tools
The industrial-grade resilience and efficiency layer for AI agents. Add semantic caching, circuit breakers, and observability to any Python tool with a single decorator — built for production-ready workflows.
Guardsman: No Diff Ships Unchallenged
An AI coding skill for Claude Code. It reads your repo's conventions, sizes every change by its blast radius, and makes the check behind non-trivial logic actually run before code ships.
Testimonials
Words from those building the future.
From builders and teams who applied these approaches in production. Honest accounts of what these systems changed, what they simplified, and what they made possible.
Contact
Got a system to build?
If you're building a RAG pipeline, an AI agent workflow, or an MCP integration and want to compare notes, swap ideas, or just avoid the mistakes I've already made — reach out.