The Agentic AI Studio

Where agents are
built, broken,
and shipped to production

Deep tutorials, system breakdowns, and real builds on Agentic AI — from multi-agent architectures to LLM pipelines to the messy reality of deploying autonomous systems at enterprise scale.

Multi-agent systems LangGraph RAG pipelines LLM evaluation Tool calling Agent memory CrewAI Autonomous agents Production AI
📚 Series — LangGraph Masterclass · 6 parts
01 Why LangGraph beats LangChain for complex agents
02 Stateful graphs — checkpoints, memory, and persistence
03 Conditional edges and dynamic routing
04 Human-in-the-loop interrupts and approvals
05 Multi-agent supervisor patterns
06 Deploying LangGraph agents to production on Azure
Building a RAG pipeline that actually works in production — not just demos
Chunking strategies, embedding choices, reranking, and the 12 ways your retrieval silently fails under real load. Every lesson from shipping RAG to enterprise.
I dissected OpenAI's Swarm framework — here's what's actually happening under the hood
Swarm looked like magic when it dropped. Three hours of reading source code later, here's the full breakdown — the handoff pattern, the context variable trick, and why it's both clever and limited.
Building an autonomous research agent with LangGraph, Tavily, and Claude — start to finish
Full build log: planning agent, web search tool, structured extraction, synthesis node, and a report generator. Every node, every edge, full code repo. This is the post I wish existed when I started.
LangGraph vs CrewAI in 2025 — an honest comparison after shipping both to production
Not a framework war. A real comparison from someone who's debugged both at 2am in production. Different tools for different problems — here's how to actually choose.
Agent memory that actually persists — a practical guide to long-term memory patterns
Semantic memory, episodic memory, procedural memory — the three memory types your agent needs, and how to implement each using vector stores, SQL, and simple key-value patterns.
The ARC-AGI-3 benchmark — what it tests and why current agents keep failing it
New reasoning benchmark, same old problem: fluid intelligence. Breaking down what it measures, how frontier models score, and what it reveals about the ceiling of current agentic approaches.
Tool calling patterns that scale — how to design agent tools without shooting yourself in the foot
Naming conventions, error handling contracts, idempotency, streaming vs. blocking — the field guide to tool design that every agentic engineer needs. With examples from a real production system.