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.
21Topics
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.
AI Agents Learning Path
Design an agent that uses tools under explicit policy, records traces, handles failure, and is evaluated against realistic tasks.
- Model the agent loop and tool contracts
- Add memory, retrieval, approval, and recovery
- Evaluate quality, safety, latency, and cost
Support Resolution Agent
Build an agent that retrieves policy, checks account context, drafts a resolution, and requires approval before a side effect.
Open the complete project guide
Tutorial Topics
Follow the lessons in order, or jump straight into the topic you need.
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2. Agent Foundations
Lesson
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3. Agent Architecture
Lesson
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4. Models & Instructions
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5. Planning & Reasoning
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6. Tools & Actions
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8. RAG & Knowledge
Lesson
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9. Multi-Agent Handoffs
Lesson
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10. Agent Interoperability
Lesson
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11. Durable Workflows
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12. Human in the Loop
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13. Computer-Use Agents
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14. Guardrails & Evaluation
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15. Observability & Tracing
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16. Security & Permissions
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17. Realtime Voice Agents
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18. Cost & Latency
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19. Production Deployment
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20. Agent Projects
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AI Agents Topic Hub
Use the full topic workspace when you want tutorials, practice, interview prep, and nearby topic links in one place.
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Tutorials, practice, and prep together
Topic hub
4 surfaces
Agent loops and tools
Memory and retrieval
Learn agent loops, model instructions, tools, memory, retrieval, planning, evaluation, safety, and production design as one practical system.
AI Agents Benefits
- 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.
