hubagenticai

practical agentic ai

Build agents that survive contact with production — and with your auditors.

Step-by-step MCP and agent tutorials, honest comparison frameworks, and real governance depth — for everyone building with agents, from your first MCP server on a laptop to systems an auditor will read. Every guide is evergreen, every code block runs.

Find your starting point Browse the learning path

learn· build· govern· copy-paste templates included

orchestrator agent:search agent:code agent:review mcp:docs mcp:git mcp:db

start with the words

What "agentic" actually means — and why a hub

agent

Software that uses an AI model to decide its next action — calling tools, reading data, checking its own work — instead of following a script you wrote in advance. A chatbot answers; an agent does.

agentic AI

The engineering discipline around agents: connecting them to tools (that's MCP), orchestrating several of them, giving them memory, and keeping them safe, observable, and affordable in production.

hub

This site — hubagenticai, the agentic AI hub — is one place where the whole path lives, first tutorial to enterprise governance, instead of forty scattered blog posts with incompatible assumptions. Learn it in order, from one voice.

why this site is different

  • Written by a practitioner, not a content farm. The author ships agentic systems inside a regulated financial enterprise — the discipline shows in every guide, but no enterprise background is required to follow any of them.
  • Every code block ran before it was published. Troubleshooting sections list errors actually hit, on real hardware.
  • Evergreen only. No model-launch news, no weekly tool roundups — guides that stay correct.
  • Production depth is real. Security, human oversight, and cost control for agents — explained so any team can apply them, from a side project to a bank. The regulatory detail lives in the Enterprise section for those who need it.

start here

Three guides that pay for the visit

Beginner

How to build your first MCP server, step by step (Python and TypeScript)

Build a working Model Context Protocol server with tools and resources, connect it to a real client, and understand what actually happens on the wire.

⏱ 45–60 min

Beginner

How an AI agent actually works: build the loop behind every framework

Every agent framework wraps the same ~40-line loop: model decides, tool runs, result feeds back. Build it in plain Python and the whole ecosystem stops being magic.

⏱ 30 min

Builder

Multi-agent orchestration explained: build an orchestrator and sub-agents from scratch

Build the agent harness pattern in ~80 lines of plain Python — an orchestrator that plans, delegates to specialist sub-agents, and synthesizes — with a mock model so you can run it instantly.

⏱ 50 min

learn

Tutorials

Build things step by step — from your first MCP server to multi-agent orchestration. Runnable code, real troubleshooting.

decide

Compare & Decide

Choosing between technologies — frameworks, models, protocols — with trade-off tables and a verdict, not vibes.

run safely

Enterprise & Governance

Security, human oversight, and cost control for agents doing real work — from team rollouts to regulated industries.

watch

Radar

Where the field is heading: a quarterly-reviewed map of what to adopt, trial, assess, or hold — with reasons.

copy

Templates

Skip the blank page: starters for MCP servers, eval harnesses, system prompts, and rollout checklists.

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