AI Agents & MCP · Developer Learning Path

AI Agents Tutorial for Developers: From Agent Loops to Production

Learn how AI agents work before hiding the important parts behind a framework. This practical course covers the agent loop, function calling, tools, memory, orchestration, MCP, security, and production architecture, with implementation-focused lessons for software engineers.

Start here if you are asking: What is an AI agent? How is an agent different from an LLM or workflow? How do agents use tools? Where does MCP fit? How do you build a small agent in Python and make it safe enough for production?

Learning map

What you will learn

AI agent fundamentalsAgent definition, loop, autonomy, tools and termination. Tools and function callingHow schemas connect model decisions to deterministic application actions. Agent architectureModel, tools, state, memory, orchestration and guardrails. Build an AI agent with PythonA small implementation that makes the control loop concrete. MCP tools, resources and promptsHow Model Context Protocol capabilities fit into agent systems.

Module 1

AI Agent Fundamentals

Build the mental model first. These lessons explain what an agent is, how the control loop works, when deterministic workflows are better, how tool calling changes an LLM application, and how architecture choices affect reliability.

1. What Is an AI Agent?A developer-friendly definition and the minimum components of an agent. 2. LLM vs AI AgentWhy a model by itself is not the same thing as an agentic application. 3. AI Agent vs WorkflowChoose between deterministic orchestration and model-directed behavior. 4. How the AI Agent Loop WorksInput, reasoning, tool selection, observation, iteration and termination. 5. Reasoning, Tools, Actions and ObservationsThe control loop behind practical tool-using agents. 6. Function Calling for AI AgentsHow schemas let models request deterministic application actions. 7. When NOT to Build an AI AgentWhere traditional software or workflows are safer, cheaper and easier. 8. Single-Agent vs Multi-Agent SystemsWhen specialization helps and when it only adds coordination overhead. 9. Agent Architecture: Model, Tools, Memory and OrchestrationA practical reference architecture for agentic applications. 10. Build Your First AI Agent in PythonCreate a small tool-using agent and understand each moving part.

Continue with MCP

Model Context Protocol for Agent Developers

MCP is not the agent itself. It is a protocol for exposing tools, resources and related capabilities to compatible clients. Once you understand the agent loop, the protocol becomes much easier to reason about.

Complete MCP courseFollow the MCP learning path from fundamentals through building servers and agent integrations. MCP fundamentalsCore protocol concepts before implementation details. MCP core capabilitiesTools, resources, prompts, schemas, elicitation, progress and caching.

AI agent FAQ

Is an AI agent just an LLM with function calling?

No. Function calling is one mechanism an agent can use. A practical agent also needs a control loop, state, tool execution, termination rules and application-level safety controls.

Should every workflow become an AI agent?

No. If the steps and decisions are known in advance, deterministic software is often more reliable and cheaper. Use an agent where model-directed decisions create real value.

Where does MCP fit?

MCP can standardize how clients discover and invoke capabilities. An agent may use MCP-provided tools, but MCP and AI agents are not interchangeable concepts.