AI Agents Tutorial
Master AI Agents from Beginner to Production
Learn AI Agents from first principles: planning, tools, memory, guardrails, evaluation, security, deployment, and production architecture for real agentic systems.
16Topics
20+Examples
FreeAlways
About AI Agents
Learn AI Agents from first principles: planning, tools, memory, guardrails, evaluation, security, deployment, and production architecture for real agentic systems.
Prerequisites
Basic Python or JavaScript, HTTP APIs, JSON, and familiarity with large language models will help you run the examples.
Who This Is For
Developers, architects, students, and technical product teams building reliable agentic applications.
What You Will Learn
Agent loops, models, tools, planning, state, RAG, handoffs, approvals, evaluation, security, tracing, cost, and deployment.
Tools Needed
A code editor, Python 3.10 or newer, a terminal, and optional model API access for adapting the provider-neutral examples.
Tutorial Topics
Follow the lessons in order, or jump straight into the topic you need.
1. Agent Foundations
Lesson
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2. Agent Architecture
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3. Models & Instructions
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4. Planning & Reasoning
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5. Tools & Actions
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7. RAG & Knowledge
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8. Multi-Agent Handoffs
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9. Human in the Loop
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10. Guardrails & Evaluation
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11. Observability & Tracing
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12. Security & Permissions
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13. Cost & Latency
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14. Production Deployment
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15. Agent Projects
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Why Learn AI Agents?
- Know when an agent is appropriate and when deterministic software is safer.
- Build controlled tool loops with state, budgets, validation, and clear stop conditions.
- Ground factual work in approved knowledge and preserve evidence through citations.
- Protect users and systems with least privilege, approval gates, and traceable decisions.
- Evaluate and operate agents using realistic datasets, production traces, cost, and latency metrics.
