Skip to main content
ToolDix logoToolDixFind, compare, and use tools
Sign In
Browse 20 learning paths

Deep-dive course

AI Agent Tutorial: Concepts to Architecture

25 lessons, roughly 399 minutes end to end. Start from what an agent actually is and build up to assembling one yourself -- with diagrams, comparison tables, and worked examples at every step, not just definitions.

Start the course

Course outline

  1. 1What Is an AI Agent?An AI agent is a system that uses a language model to pursue a goal across multiple steps -- deciding what to do next, taking an action, and reacting to the result, rather than answering in one shot.Beginner12 min
  2. 2AI Agents vs. Chatbots: What Actually ChangesChatbots and agents both sit on top of a language model, but they differ in how many steps they take, whether they can act on the world, and what "done" means.Beginner11 min
  3. 3Core Components of an Agent SystemEvery working agent is built from the same small set of parts -- an LLM core, tools, memory, retrieval, an orchestrator, and guardrails -- assembled differently for different tasks.Beginner14 min
  4. 4AI Agent Terminology: A Working GlossaryA reference glossary of the terms that show up constantly in agent documentation and papers -- tool call, context window, orchestration, grounding, and more -- defined in plain language.Beginner12 min
  5. 5How an Agent Works: Inside the Agent LoopThe mechanics of a single agent turn, step by step -- from receiving a goal, through planning and tool calls, to deciding whether to continue or stop.Intermediate14 min
  6. 6The Architecture Stack Under an AgentEvery agent depends on infrastructure layers beneath the model -- how models are served, how tools are routed, how state is persisted, and how safety guardrails are enforced.Intermediate15 min
  7. 7Agent Architecture PatternsA survey of the recurring architecture patterns behind production agents -- single-agent loops, planner-executor splits, orchestrator-worker teams, and reflection -- and when each one earns its added complexity.Intermediate16 min
  8. 8LLM Foundations Every Agent Builder NeedsThe essentials of how large language models work -- next-token prediction, the transformer architecture, and training stages -- as far as an agent builder actually needs to go.Intermediate14 min
  9. 9Tokens: How Agents See TextWhat tokens are, how different tokenizers split text differently, why token counts vary across models and languages, and how to count tokens accurately for cost and truncation planning.Beginner12 min
  10. 10Context Windows and Their LimitsWhat a context window is, how it differs from tokens as a concept, how context fills up in long agent loops, and practical strategies for managing it (truncation, summarization, sliding windows).Beginner12 min
  11. 11Multimodal Models in Agent SystemsHow agents that see images, read documents, or process audio differ from text-only agents, and where multimodal input actually changes agent design.Intermediate15 min
  12. 12Prompt Engineering for AgentsPrompt engineering for an agent's system prompt and tool descriptions differs from prompting a chatbot -- it has to hold up across many unattended steps, not just one reply.Intermediate16 min
  13. 13Reasoning and Planning StrategiesChain-of-thought, ReAct, and tree-of-thought are the three reasoning strategies that show up most in agent design -- what each one adds, and what it costs.Advanced16 min
  14. 14Vector Databases and Embeddings for AgentsHow embeddings turn text into comparable numbers, how vector databases use that to power fast similarity search, and how agents rely on both for memory and retrieval.Intermediate14 min
  15. 15Retrieval-Augmented Generation (RAG) for AgentsHow RAG lets an agent answer using information beyond its training data -- the four-step pipeline, why chunking matters, and where RAG fits inside a larger agent loop.Intermediate16 min
  16. 16Context Engineering for AgentsContext engineering is the discipline of deciding what actually goes into a model's limited context on each turn -- system prompt, memory, retrieved facts, and history -- and it matters more than prompt wording alone as agents scale.Advanced15 min
  17. 17Tool Calling: Giving Agents HandsHow models decide when and how to call tools using structured schemas and dispatch loops. Covers tool definition, the mechanics of function-calling models, and the patterns for routing tool calls.Intermediate18 min
  18. 18Memory Systems for AgentsHow agents retain information across conversation turns. Covers conversation buffers (short-term), context compression, and persistent storage patterns (long-term memory with key-value and vector-backed approaches).Intermediate20 min
  19. 19Connecting Agents to External SystemsPatterns for safely integrating agents with external APIs, databases, and services. Covers authentication, rate limiting, error recovery, and monitoring third-party integrations.Intermediate19 min
  20. 20Multi-Agent Systems and CoordinationHow multiple agents work together. Covers agent-to-agent communication patterns, orchestrator models, task delegation, and handling coordination failures.Advanced21 min
  21. 21Building Your First Agent: A Worked ExampleA worked example that assembles every component from this course -- goal, tools, loop, memory, and guardrails -- into one small agent, plus a survey of open-source frameworks to build on.Intermediate16 min
  22. 22Evaluating and Testing AgentsMethods for measuring agent performance, building test suites, and comparing agent designs. Covers metrics, benchmarking, and how to detect regressions.Advanced20 min
  23. 23Agent Safety, Guardrails, and AlignmentHow to prevent agents from making harmful decisions. Covers input validation, action filtering, monitoring, and recovery from policy violations.Advanced22 min
  24. 24Deploying Agents to ProductionHow to take agents from local testing to running reliably in production. Covers containerization, scaling, monitoring, and incident response.Advanced21 min
  25. 25Vertical Use Cases: Where Agents Are Used TodayReal-world applications of agents across industries. Concrete examples from customer support, software engineering, research, and finance showing how agents solve specific problems.Beginner18 min

What this course covers

Every lesson includes at least one comparison table or worked code example, and several include an original ToolDix diagram of the agent loop, agent architecture, the RAG pipeline, or context window composition. Each lesson also cites the primary sources it draws from -- Anthropic's engineering guidance, the original RAG and ReAct papers, and current provider documentation -- so you can go deeper on any single topic.