Is the AI Agents Academy completely free?

Yes, 100% free. No sign-up, no credit card. All 13 modules, 58 lessons, 9 terminal labs, and 115 quiz questions are accessible immediately. Your progress saves locally in your browser.

How is Infinity AI Agents Academy different from vendor-locked agent courses?

Unlike single-framework platforms (such as LangChain Academy or CrewAI University) that tie you to a single proprietary ecosystem, Infinity Tech Stack's AI Agents Academy is a 100% free, vendor-neutral engineering curriculum. It teaches the complete production stack across ReAct loops, LangGraph, CrewAI, Google ADK, OpenAI Agents SDK, A2A protocol, and Model Context Protocol (MCP) integration with in-browser terminal labs and zero sign-up required.

What topics does the AI Agents Academy cover?

The course covers agent fundamentals, ReAct loops, Plan-and-Solve, tool use & function calling, MCP integration, Agentic RAG, multi-agent systems (CrewAI, LangGraph), A2A protocol, Google ADK, agent memory, safety & guardrails, red teaming, CI/CD for agents, and production observability.

What is the A2A protocol?

A2A (Agent-to-Agent) is Google's open protocol for enabling interoperability between AI agents. It allows agents from different vendors and frameworks to discover each other, exchange capabilities via Agent Cards, and delegate tasks securely.

Google ADK (Agent Development Kit) is an open-source framework for building, orchestrating, and deploying AI agents. It supports Python, Go, Java, and TypeScript, and integrates with MCP for tools and A2A for agent-to-agent communication.

Do I need any prerequisites?

Basic programming knowledge is helpful but not required. The course starts from absolute fundamentals (what is an agent?) and progressively advances to expert-level topics like multi-agent orchestration and production safety.

Does the AI Agents Academy include hands-on labs?

Yes. Hands-on terminal labs are built into the lessons - build the minimal agent loop, scaffold a CrewAI crew, and launch LangGraph dev using real, vendor-verified commands, with objectives, hints, solutions, and XP rewards. No sign-up or setup needed.

ReAct: Reason + Act Tutorial | The Agentic Control Loop

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ReAct: Reason + Act

🔄 The Agentic Control Loop⏱ 8 min⭐ 60 BASE XP⌨ HANDS-ON LAB

The ReAct Pattern

ReAct (Reasoning + Acting) is the foundational pattern for modern AI agents. Instead of just answering a question, the model emits a "Thought", then an "Action". The system runs the action and returns an "Observation".

Thought: I need to find the current price of AAPL. I will use the search_finance tool.
Action: search_finance(ticker="AAPL")
Observation: $195.50
Thought: Now I have the price. I can answer the user.
Answer: The current price of AAPL is $195.50.

💡 Key Insight: ReAct works because forcing the model to write out its "Thought" (Chain-of-Thought) before predicting the "Action" drastically reduces errors and hallucinated tool calls.

⌨ HANDS-ON LABSimulate ReAct Thought-Action-Observation Loop

⭐ +150 XP

Step through a ReAct execution cycle validating prompt parsing of Thought, Action, Action Input, and Observation.

1Simulate a ReAct trace execution.

lab-sandbox — simulated environment

INFINITY LAB SANDBOX v2.6 — simulated shell
Type the command for the current objective. Helpers: "hint", "solution", "clear".
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OBJECTIVE 1 / 1 — type "hint" if stuck

SYNAPSE VERIFICATION

QUERY 1 // 1

In the ReAct pattern, what is the sequence of steps?

Act → Observe → Reason

Reason → Act → Observe

Observe → Answer → Quit

Think → Code → Test