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AI Agents: From Architecture to Production — AI Academy

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AI Agents: From Architecture to Production

A practical course on multi-agent AI systems based on Microsoft's AI Agents for Beginners curriculum. Covers the agent loop, tool calling, memory patterns, orchestrator–worker architecture, MCP and A2A protocols, agent evaluation, guardrails, and production deployment with observability.

3modules

9lessons

325 mintotal time

Developers building reliable multi-agent AI systemsaudience

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Module 1

AI Agent Architecture

How the agent loop works, how agents differ from chatbots, how LLMs make decisions through tool calling, and what memory patterns exist.

The Agent Loop: Perception → Reasoning → Action

Breaking down the perception → reasoning → action → observation cycle, how LLMs make decisions inside an agent, and why agents are fundamentally different from chatbots.

Tool Calling: Deep Dive

Defining tools with JSON Schema, the full tool_call → tool_result cycle, error handling, and parallel tool calls.

Agent Memory Patterns

Four memory types — in-context, semantic (vector DB), episodic (past runs), procedural (instructions) — and criteria for choosing the right one.

Module 2

Multi-Agent Systems

The orchestrator–worker pattern, MCP for tool connectivity, and A2A for agent-to-agent communication.

Orchestrator–Worker Pattern

Task decomposition, routing to specialized workers, result aggregation. Python pseudocode using the Anthropic SDK.

Model Context Protocol (MCP)

What MCP is, why it exists, MCP servers vs clients, using existing MCP servers, and writing a minimal MCP server in Python.

Agent-to-Agent (A2A) Protocol

Google's standard for inter-agent communication: agent cards, task lifecycle, comparing MCP vs A2A, and when to use which.

Module 3

Agents in Production

Evaluating agent quality, defending against attacks and controlling costs, observability, and monitored deployment.

Evaluating Agents

Trajectory eval, tool-use eval, final output eval, LLM-as-judge. The ragas framework and custom evaluators.

Agent Guardrails

Prompt injection defense, output validation, human-in-the-loop gates, cost budgets.

Production Deployment

Observability (traces, spans), retry/fallback logic, cost management, monitoring dashboards.

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Updated · May 14, 2026