The Agentic AI Studio

The future of AI
is agentic.

Deep tutorials, system breakdowns, and real builds on Agentic AI — from multi-agent architectures to production deployments. Written by a practitioner.

Start reading → How to build agents
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Foundation

What is
Agentic AI?

Agentic AI is the architecture shift where AI stops answering questions and starts completing goals. An agent perceives its environment, reasons about the best action, uses tools, and acts — in a continuous loop — until the objective is met.

It's the difference between a search engine and a research assistant. Between a calculator and a co-worker. The models haven't changed. The architecture has.

Learn to build agents ›
01
Perceive & Reason
The agent reads context — documents, APIs, history — and uses an LLM to reason what to do next. Not just retrieve, but think.
02
Use Tools
Agents call functions: search, query databases, write code. Tools transform language models from advisors into actors.
03
Loop & Plan
Unlike single prompts, agents run in a loop — plan, act, observe, re-plan. They handle tasks no single call can complete.
04
Coordinate
Complex tasks split across specialist agents — planner, researcher, critic, executor — that pass state like a team.
Principles

Best ways to build
Agentic AI

Distilled from 11 years shipping production AI — not theory, not hype. What actually works when your agent meets real users.

01
🕸️
Start with a graph, not a chain
Linear chains break the moment a step fails or needs to branch. Model your agent as a directed graph — nodes are actions, edges are conditions. Structure matters more than framework.
LangGraphArchitecture
02
🛡️
Design for failure, not the happy path
Agents fail in ways chains never do: tool timeouts, hallucinated calls, infinite loops, context overflow. Build a critic node. Add escape hatches. Plan for the 2am incident.
ReliabilityError handling
03
🔧
Keep tools small, typed, idempotent
An agent's quality ceiling is the quality of its tools. Each tool should do one thing, return structured output, and be safe to call twice. Name them like functions — "search_web" beats "web".
Tool designFunction calling
04
🧠
Use RAG for knowledge, not memory
RAG retrieves facts. Memory retains context. Conflating them breaks both. Use a vector store for domain knowledge, a structured store for episodic and working memory — separately.
RAGMemoryVector DB
05
📊
Evaluate outputs before users see them
Don't eyeball evals. Run an LLM-as-judge in CI on every build. Track faithfulness, relevance, and tool call accuracy. A regression in eval score is the first sign something broke.
EvaluationLLM-as-judgeCI/CD
06
👤
Human-in-the-loop before irreversible actions
Autonomous doesn't mean unsupervised forever. For irreversible actions — sending emails, modifying databases — build in an approval interrupt. Trust is earned incrementally.
SafetyHITLLangGraph
Content

Latest articles

Browse all articles ›
TutorialDec 2024
Building a RAG pipeline that actually works in production
Chunking, embeddings, reranking, and the 12 ways your retrieval silently fails under real load.
14 min read→
BreakdownDec 2024
I dissected OpenAI's Swarm — here's what's actually happening
The handoff pattern, the context variable trick, and why it's both clever and limited.
11 min read→
BuildNov 2024
Autonomous research agent with LangGraph, Tavily, and Claude
Full build log: planner, search tool, extractor, synthesizer. Every node, every edge, full repo.
22 min read→
OpinionNov 2024
LangGraph vs CrewAI — an honest comparison after shipping both
Not a framework war. A real comparison from someone who's debugged both at 2am in production.
7 min read→
TutorialOct 2024
Agent memory that actually persists
Semantic, episodic, procedural — the three memory types and how to implement each.
16 min read→
BreakdownOct 2024
ARC-AGI-3 — why current agents keep failing the benchmark
Fluid intelligence, not data memorization. What the benchmark reveals about today's agents.
9 min read→
Series

LangGraph Masterclass

See all series ›
Episode 01
Why LangGraph beats LangChain for complex agents
The graph mental model and why it unlocks what chains can't.
Published · 12 min
›
Episode 02
Stateful graphs — checkpoints, memory, persistence
How to make your agent remember across runs without a database.
Published · 15 min
›
Episode 03
Conditional edges and dynamic routing
Building decision logic that routes between nodes based on agent output.
Published · 10 min
›
Episode 04
Human-in-the-loop interrupts and approvals
Pausing agent execution for human review before irreversible actions.
Coming soon
›
Episode 05
Multi-agent supervisor patterns
Orchestrating specialist agents from a central planning supervisor.
Coming soon
›
Episode 06
Deploying LangGraph agents to production on Azure
From local dev to enterprise-grade deployment — architecture patterns that hold.
Coming soon
›
Core concepts

Essential knowledge for every
agentic engineer

💭Reason
⚡Act
👁Observe
Concept 01
The ReAct loop — the heartbeat of every agent
The foundational pattern behind every modern agent. The LLM reasons what to do, calls a tool (acts), receives an observation, then reasons again. This loop is prerequisite to debugging any agent that behaves unexpectedly.
Read the deep dive ›
🔍search_web(query)
📄read_document(id)
🐍run_python(code)
📧send_email(to, body)
🗄️query_db(sql)
Concept 02
Tool design is an art
Your agent's quality ceiling is the quality of its tools. Small, typed, idempotent functions with clear names. "search_web" beats "web". Structured outputs beat free text. The LLM picks tools by name — naming is API design.
Read the guide ›
Semantic
Episodic
Working
Procedural
Concept 03
The four types of agent memory
Semantic memory stores facts. Episodic memory stores experiences. Working memory holds the current context. Procedural memory encodes skills. Each needs a different store — vector DB, SQL, in-context, and code respectively.
Read the tutorial ›
N
Nagesh CH
Senior AI/ML Architect · 11 years in production AI
Building Agentic AI systems at enterprise scale since before "agentic" was a buzzword. I write about what actually works — and what quietly fails — when you ship autonomous agents to production.
Portfolio › LinkedIn › GitHub ›