Part 1 — Agent Fundamentals

11 chapters · 28.3 study hours allocated in the Study Plan · 5 slide decks · 52 videos · 53 code example files

On this page
  1. Chapters
  2. Chapter summaries
    1. 1.1A. UI Foundations
    2. 1.1B. Human-in-the-Loop Patterns and Accessible Design
    3. 1.2. Core Patterns
    4. 1.3. Multi-Agent Systems
    5. 1.4. Memory & Perception
    6. 1.5A. Stateful Orchestration - Foundations
    7. 1.5B. Stateful Orchestration - Worked Examples
    8. 1.6. Stateful Orchestration - Pitfalls, Integration, and Synthesis
    9. 1.7A. Relational Reasoning with Knowledge Graphs
    10. 1.7B. Relational Reasoning with Knowledge Graphs - Hybrid RAG+KG Integration
    11. 1.8. Agent Resilience and Synthesis
    12. Additional worked examples
    13. Labs

Chapters

Rating tags show which certification knowledge maps rate the chapter H (highly relevant) in at least one item: NV NCP-AAI · AWS AIP-C01 · DBX Databricks GenAI Engineer · GCP Professional ML Engineer · MS AI-102. See Certifications.

Ch. Title Hours Slides Quiz Videos Figures Code H-rated for
1.1A UI Foundations 4.8 PDF‡ Quiz 7 — — NV AWS DBX MS
1.1B Human-in-the-Loop Patterns and Accessible Design 1.8 PDF‡ Quiz 4 — 2 NV AWS GCP MS
1.2 Core Patterns 3.6 PDF‡ Quiz 4 — — NV AWS GCP MS
1.3 Multi-Agent Systems 7.3 PDF‡ Quiz 5 — 9 NV MS
1.4 Memory & Perception 1.0 PDF‡ Quiz 4 — 3 NV AWS GCP MS
1.5A Stateful Orchestration - Foundations 2.2 PDF† Quiz 5 — — NV AWS GCP MS
1.5B Stateful Orchestration - Worked Examples 1.4 PDF† Quiz 5 — 8 NV AWS MS
1.6 Stateful Orchestration - Pitfalls, Integration, and Synthesis 1.8 PDF‡ Quiz 4 — 7 NV AWS GCP MS
1.7A Relational Reasoning with Knowledge Graphs 2.7 PDF‡ Quiz 7 — 12 NV AWS GCP MS
1.7B Relational Reasoning with Knowledge Graphs - Hybrid RAG+KG Integration 1.7 PDF‡ Quiz 7 — 5 NV AWS GCP MS
1.8 Agent Resilience and Synthesis § — PDF‡ — — — 7 NV

Notes. The Videos column counts the videos shown under each chapter summary below, out of the unique direct links in Part_01_YoutubeVideos.md (“3 of 5”). A video is left out when its link is dead, embedding is disabled, or YouTube’s title does not match the entry; see the link check. Chapters can also list search suggestions instead of links.

† Linked by chapter-family number, not an exact ID match: the deck, quiz, or figure set is numbered differently from this chapter in the source files (for example a quiz or deck numbered 6.2 for chapters 6.2A and 6.2B).

‡ A combined deck that covers more than one chapter.

A chapter that is missing from a certification’s mapping file shows no tag for that certification: the NVIDIA file omits 4.1 and 10.6, and the other four omit 1.8, 9.16, and 9.17.

§ Has no section of its own in Study_Plan.md; the title comes from the Study Plan’s table of contents or a cross-reference there, or (9.16, 9.17) from the quiz list.

Slides for Parts 1–2 are the Agentic AI Book Club session decks, each covering two or three chapters.

Chapter summaries

Summaries are excerpted from Study_Plan.md, which also lists each chapter’s key concepts and self-check questions.

1.1A. UI Foundations

This chapter establishes the fundamental differences between traditional application UIs and agent UIs by centering on agent autonomy. Agents make independent decisions with real-world consequences, creating unique design challenges where users transition from operators to overseers. The chapter introduces foundational principles (progressive disclosure, transparency, control, error communication, context awareness) and UI patterns (chat, command palette, approval workflows) that form the basis for building trustworthy agent interfaces.

Videos (7)

Progressive Disclosure · hosted outside YouTube, so it cannot be embedded

Managing Visual Complexity in Applications and Websites · hosted outside YouTube, so it cannot be embedded

Principles of Human-Centered Design · hosted outside YouTube, so it cannot be embedded

1.1B. Human-in-the-Loop Patterns and Accessible Design

This chapter addresses the fundamental challenge of autonomous agent systems by calibrating human intervention to match decision risk. It establishes three core control patterns (notification, approval, monitoring) distributed across a spectrum, provides decision frameworks for pattern selection, and introduces WCAG-based accessible design ensuring all users can interact effectively with approval workflows and agent systems.

Videos (4)
Introduction to Web Accessibility and W3C Standards · W3C Web Accessibility Initiative (WAI)
Web Accessibility Perspectives: Colors with Good Contrast · W3C Web Accessibility Initiative (WAI)
Code examples (2 files)

1.2. Core Patterns

This chapter explores four fundamental agent reasoning patterns—ReAct (Reasoning + Action), Plan-and-Execute (Hierarchical Task Decomposition), Reflection (Self-Critique and Iterative Refinement), and Tool-Use Architecture—examining their strengths, limitations, and production applicability. The chapter emphasizes evidence-based pattern selection over assumptions, exposing common misconceptions and providing clear guidance on when each pattern optimizes performance versus when they create unnecessary cost and complexity.

Videos (4)

1.3. Multi-Agent Systems

This chapter addresses the fundamental challenge of coordinating multiple autonomous agents toward shared or competing objectives. It examines collaborative paradigms using specialized agents with shared goals, competitive systems applying game-theoretic principles, swarm intelligence emerging from simple local rules, communication mechanisms (message passing, shared memory, event-driven, API-based), and orchestration patterns (centralized, decentralized, hierarchical, federated) with explicit failure modes and selection criteria for each approach.

Videos (5)
Code examples (9 files)

1.4. Memory & Perception

This chapter establishes the architectural foundation for agent cognition through memory and perception systems. It distinguishes between short-term working memory (context window) and long-term systems (semantic, episodic, procedural), introduces perception pipeline stages, and addresses critical integration challenges including the vector store misconception, temporal synchronization, context degradation, and the “lost in the middle” effect. The chapter emphasizes that proper memory-perception integration enables context-aware agent behavior essential for production systems.

Videos (4)
RAG From Scratch · LangChain
Code examples (3 files)

1.5A. Stateful Orchestration - Foundations

Establishes the theoretical foundations of stateful orchestration by defining core concepts including logic trees as decision path structures, prompt chains as sequential orchestration patterns, and stateful orchestration as explicit context management across execution cycles. The chapter introduces the Stateful Agent Orchestration Model organizing State Storage, Logic Tree Evaluation, and Execution Engine subsystems, while teaching three architectural principles (separation of state and logic, explicit transitions, and idempotent operations) that enable production-grade reliability.

Videos (5)

1.5B. Stateful Orchestration - Worked Examples

Demonstrates stateful orchestration principles through concrete implementations comparing stateless versus stateful agent architectures. Uses a multi-city flight booking example to expose failure modes of stateless designs (context loss on error, latency multiplication, observability gaps, parallelization impossibility) and shows how stateful orchestration addresses each. A customer support routing example demonstrates logic tree implementation using LangGraph with TypedDict state schemas and conditional edges, showing how explicit graph representation enables visualization, modification, and performance optimization through infrastructure choices like NVIDIA NIM.

Videos (5)
Code examples (8 files)

1.6. Stateful Orchestration - Pitfalls, Integration, and Synthesis

Addresses production failures and integration patterns emerging when implementing stateful orchestration at scale. Covers critical misconceptions (LLMs are stateless despite seeming to remember context), failure modes (unbounded state growth, infinite loops, sequential execution of parallelizable operations), and how stateful orchestration implements patterns from Chapter 1.2. Demonstrates how orchestration foundations enable advanced capabilities like hierarchical planning, continual replanning, collaborative planning, and memory systems.

Videos (4)
LangGraph: Intro · LangChain
Code examples (7 files)

1.7A. Relational Reasoning with Knowledge Graphs

Introduces knowledge graphs as structured representations of entities, relationships, and properties, addressing limitations of vector-based retrieval systems where relationships are implicit in embeddings. Covers property graphs as flexible knowledge representation models, Cypher query language for pattern matching and multi-hop traversal, and knowledge graph construction from unstructured documents through NER, entity disambiguation, and relationship extraction. Demonstrates how knowledge graphs complement vector RAG for questions requiring explicit relationship traversal and multi-hop reasoning.

Videos (7)
Neo4j Course for Beginners · freeCodeCamp.org
Code examples (12 files)

1.7B. Relational Reasoning with Knowledge Graphs - Hybrid RAG+KG Integration

Addresses when and how to combine vector RAG with knowledge graph traversal through three hybrid integration patterns. Covers decision criteria distinguishing simple factual queries (RAG alone) from multi-hop relational queries (graph alone) and hybrid queries requiring both. Demonstrates the compliance analysis system combining semantic understanding with relationship verification, analyzes performance trade-offs (50% latency overhead for hybrid), and covers production deployment patterns including knowledge graphs as memory backends, tool invocation enhancers, and multi-agent coordination infrastructure. Emphasizes optimization strategies and operational disciplines maintaining system health at scale.

Videos (7)
Neo4j Course for Beginners · freeCodeCamp.org
Code examples (5 files)

1.8. Agent Resilience and Synthesis

No summary in the Study Plan for this chapter.

Code examples (7 files)

Additional worked examples

From more_examples/part_01/:

Labs

No finished lab exists for this Part yet. These legacy example files are prose excerpts with embedded code, kept as source material; they do not count as lab coverage. See Labs.