Instructor-led 4-days

Detailed Course Description

As AI matures, the “Single Agent” model is being replaced by Multi-Agent Systems (MAS). This course provides a deep-dive into the architectural patterns required to manage multiple AI entities working in concert. We move from simple linear pipelines to complex, non-linear Agentic Graphs.

Participants will learn how to manage state synchronization, solve inter-agent conflicts, and implement hierarchical oversight. We use high-performance visual orchestration layers to build production-ready systems that can plan, execute, and self-correct. By the end of this course, you will be able to architect an entire “department” of AI agents capable of handling enterprise-scale workflows with minimal human intervention.

Key Real-World Takeaways

• Architectural Fluency: The ability to design complex, non-linear agent systems that handle real-world ambiguity and “messy” data.
• Operational Reliability: Implementation of “Redundancy” (multiple agents checking one task) to achieve enterprise-grade accuracy.
• Digital Workforce Management: Skills to manage “AI Employees” just as you would manage a human team—setting goals, monitoring performance, and providing feedback.
• Vendor Agnosticism: Mastery of the logic of orchestration, allowing you to build on any platform (Microsoft, OpenAI, LangGraph, or custom No-Code).

Module 1: The Orchestration Blueprint

Description: Understanding the move from “Chains” to “Graphs.” We define the logic of the Supervisor Agent and how it differs from a standard worker.

• Lab: The Dispatcher Pattern | Skill: Intent-based routing and task delegation.
• Lab: The Deconstructor | Skill: Breaking monolithic goals into parallel sub-tasks.
• Lab: Sequential Hand-off Protocol | Skill: Maintaining data integrity across agent boundaries.

Module 2: Advanced System Personas

Description: Tuning the “Psychology” of a multi-agent team. We explore how to create “Cognitive Diversity” within a swarm to avoid groupthink.

• Lab: The Adversarial Auditor | Skill: Creating “Red Team” agents to find flaws in logic.
• Lab: The Specialist Bureau | Skill: Hardening system prompts for hyper-specific domain expertise.

Module 3: Global vs. Local State Management

Description: Solving the “Memory Problem.” How do agents share a “Blackboard” of information without overwhelming their context window?

• Lab: The Shared Memory Blackboard | Skill: Implementing centralized data storage for agent teams.
• Lab: Context Pruning & Trimming | Skill: Optimizing token usage in long-running agent loops.

Module 4: Dynamic Tool Selection & Execution

Description: Teaching agents to choose their own weapons. We focus on the decision logic behind choosing a specific API or database at the right moment.

• Lab: The API Negotiator | Skill: Autonomous tool-calling based on real-time reasoning.
• Lab: The Web-Scraping Swarm | Skill: Orchestrating multiple agents to harvest and clean data.

Module 5: Agentic RAG (The Knowledge Layer)

Description: Moving from static retrieval to iterative discovery. Agents learn to “critique” what they find and search again if the answer is insufficient.

Lab: The Iterative Researcher | Skill: Building self-correcting retrieval loops.
Lab: Multi-Document Synthesis | Skill: Cross-referencing data across 100+ disjointed sources.

Module 6: Cognitive Conflict & Consensus

Description: What happens when agents disagree? We implement “voting” and “debate” structures to find the most accurate path forward.

• Lab: The Debate Chamber | Skill: Using multi-agent disagreement to improve output accuracy.
• Lab: Majority Rule Consensus | Skill: Implementing “Voting” logic for classification tasks.

Module 7: Self-Reflection & Error Handling

Description: Building agents that can “Think about their Thinking.” This module focuses on self-healing loops when an agent hits a dead end.

• Lab: The Self-Correction Loop | Skill: Designing “Reflection” steps where agents critique their own work.
• Lab: The Troubleshooting Agent | Skill: Autonomous error-code handling and API retries.

Module 8: Visual Graph Architecture

Description: Designing the “Flow” of the swarm. We explore Directed Acyclic Graphs (DAGs) and how to visualize agentic movement.

Lab: The Workflow Visualization | Skill: Mapping complex agent interactions in a node-based UI.
Lab: Branching Logic Gates | Skill: Designing conditional paths based on agent sentiment.

Module 9: Inter-Agent Communication (The “Bus”)

Description: How agents talk to each other. We explore the protocols for “passing the baton” between specialized AI units.

• Lab: The Internal Briefing | Skill: Standardizing data formats for inter-agent communication.
• Lab: The Query Refiner | Skill: Agent A improving the “User Prompt” for Agent B.

Module 10: Human-in-the-Loop (HITL) Advanced Patterns

Description: Implementing “Safety Gates.” We design systems that only proceed when a human provides a “Digital Signature.”

• Lab: The Approval Dashboard | Skill: Building a UI for humans to review and edit agent thoughts.
• Lab: The Escalation Trigger | Skill: Automated hand-off to a human for high-uncertainty tasks.

Module 11: Multi-Model Orchestration

Description: Using the right tool for the job. Why use GPT-4 for everything? We learn to mix models (Claude for writing, GPT for logic, Gemini for vision).

• Lab: The Hybrid Swarm | Skill: Coordinating different LLM providers in a single workflow.
• Lab: Model Benchmarking | Skill: Testing which model performs best for specific agent roles.

Module 12: Vision & Multimodal Agency

Description: Agents that can “See.” Connecting vision-capable models into the multi-agent orchestration.

• Lab: The Visual Auditor | Skill: Converting images/diagrams into actionable agent tasks.
• Lab: Multimodal Reporting | Skill: Generating text reports combined with automated image generation.

Module 13: Scalability & Performance Tuning

Description: Managing a “City” of agents. How do we keep latency low and throughput high when dozens of agents are working?

• Lab: Parallel Task Execution | Skill: Reducing latency by running independent agents in sync.
• Lab: Token Budget Governance | Skill: Hard-limiting agent costs to prevent infinite loops.

Module 14: Security, Privacy, & Guardrails

Description: Protecting the enterprise. We implement PII filters and prevent “Prompt Injection” across the swarm.

• Lab: The PII Scrubbing Agent | Skill: Filtering sensitive data before it reaches the LLM.
• Lab: The Prompt Firewall | Skill: Detecting malicious intent in user queries before they hit the swarm.

Module 15: Auditing & Observability

Description: “Why did the agent do that?” We build the logs and traces necessary to explain agent behavior to stakeholders.

• Lab: The Traceability Log | Skill: Creating transparent “Reasoning Logs” for every agent action.
• Lab: Performance Dashboarding | Skill: Visualizing agent success rates and ROI metrics.

Module 16: The Capstone: The Autonomous Enterprise

Description: The final project. Participants build a fully functional “Digital Department” from scratch.

• Lab: The Market Intelligence Department | Skill: Architecting a 5-agent team for 24/7 competitor monitoring.
• Lab: The Support-to-Sales Bridge | Skill: Connecting support agents to sales agents for automated upselling.
• Lab: The Software Dev Swarm | Skill: A team that writes, tests, and documents a small script autonomously.
• Lab: The Final Governance Review | Skill: Auditing the entire capstone for cost, safety, and accuracy.