Duration: 3 Days
Format: 100% Lecture & Architecture Deep Dive
Level: Foundational

This course focuses entirely on the theory and architecture of Agentic AI. It is ideal for professionals who want to understand the design principles behind autonomous AI systems, including system patterns, protocol standards, workflow logic, state management, governance, and ethical boundaries.

Course Outline

Day 1: Multi-Agent Architectures & Orchestration

Objective: Master the transition from linear prompting to complex agent ecosystems.

Morning: Foundations of Multi-Agent Systems (MAS)

• The shift from single-model chains to multi-agent ecosystems.

• Why the “small models, big system” philosophy is emerging.

• Architectural topologies:

  • Fully connected.

  • Star / hub-and-spoke.

  • Manager-worker.

Afternoon: Orchestration Frameworks & Logic

• Cyclic vs. acyclic graphs and why loops matter in agentic reasoning.

• Role-based design using persona, goal, and backstory.

• Planning approaches:

  • ReAct.

  • Plan-and-Execute.

  • Chain-of-Thought.

Day 2: Communication Protocols & Memory Engineering

Objective: Standardize agent interactions and manage persistent intelligence.

Morning: The Connectivity Layer

• Model Context Protocol (MCP) and its role in making tools and data sources pluggable.

• Agent-to-Agent (A2A) communication standards.

• Task hand-offs, refusals, and structured payload exchange.

• The discovery problem in multi-agent ecosystems.

Afternoon: State, Context, and Vector Memory

• Ephemeral memory and context window management.

• Working memory and checkpointing in long-running loops.

• Long-term memory using vector databases and retrieval-augmented reasoning.

• State persistence for human-in-the-loop workflows across days or weeks.

Day 3: Autonomy, Governance, and Human Integration

Objective: Define the boundaries of autonomous action and safety protocols.

Morning: High-Level Autonomy & Agentic Loops

• Self-correction cycles and termination conditions.

• Preventing infinite loops and hallucination spirals.

• Goal alignment across delegated subtasks.

Afternoon: Human-in-the-Loop and Ethics

• Human-in-the-loop design patterns:

  • Input-gate.

  • Approval-gate.

  • Feedback-loop.

• Governance considerations including audit logs, traceability, and explainability.

• Understanding why an agent took a specific autonomous path.

Key Learning Outcomes

By the end of this course, participants will be able to:

• Diagram complex agent workflows for non-linear tasks.

• Understand state management in multi-turn AI interactions.

• Apply strategic reasoning to autonomous and semi-autonomous design patterns.

• Evaluate the role of memory, orchestration, and governance in agentic systems.

• Identify when human oversight is required in autonomous workflows.

• Design agent architectures that balance capability, safety, and control.