Course Outline
Day 1
Anatomy of a Modern AI Agent
Moving beyond chatbots to understand agents as autonomous reasoning and acting systems.
Exploration of reactive, proactive, hybrid, and goal-directed agent paradigms.
Core components: perception, planning, memory, tool use, and action.
Evaluating design tradeoffs between single-agent and multi-agent systems.
Agent Frameworks and the Modern Stack
Analyzing LangChain, LlamaIndex, AutoGen, CrewAI, and their respective tradeoffs.
Comparing modern tools with classical frameworks like JADE and SPADE.
Selecting the appropriate framework based on production requirements.
Understanding tool calling, function calling, and structured outputs.
Hands-on activity: scaffolding a single Python agent with tool calls.
Multi-Agent System Architectures
Overview of centralized, decentralized, hybrid, and layered MAS designs.
FIPA ACL, message-passing protocols, and their modern equivalents.
Coordination patterns: planning, negotiation, and synchronization.
Emergent behavior and self-organization within agent populations.
Decision-Making and Learning in Agents
Applying game theory to cooperative and competitive agent interactions.
Implementing reinforcement learning in multi-agent environments.
Facilitating transfer learning and knowledge sharing across agents.
Managing conflict resolution and trust among coordinating agents.
Day 2
Multi-Modal Foundations for Agents
Integrating multi-modal AI as a unified workflow across text, image, speech, and video.
Examining leading multi-modal models: GPT-4 Vision, Gemini, Claude, Whisper.
Fusion techniques for combining modalities within an agent's reasoning loop.
Evaluating latency, cost, and accuracy tradeoffs in multi-modal pipelines.
Building the Perception Layer
Image processing for agents: classification, captioning, and object detection.
Speech recognition using Whisper ASR and streaming transcription.
Text-to-speech synthesis for natural voice interaction.
Connecting perception outputs to LLM-driven reasoning and tool selection.
Hands-On - Building a Multi-Modal Agent in Python
Defining the agent's task, context window, and tool inventory.
End-to-end integration of GPT-4 Vision and Whisper APIs.
Implementing memory, state management, and conversation handling.
Adding safe tool calls that produce real-world side effects.
Hands-On - Orchestrating a Multi-Agent System
Composing specialized agents using AutoGen or CrewAI.
Defining roles, responsibilities, and inter-agent communication protocols.
Managing resource allocation and coordination in simulated environments.
Logging agent reasoning, tool calls, and decisions for inspection and audit.
Day 3
Threat Surface of Production AI Agents
Understanding what makes agentic AI uniquely vulnerable compared to traditional software.
Analyzing the attack surface: data, model, prompt, tool, output, and interface layers.
Conducting threat modeling for agent-based systems with autonomous tool use.
Comparing AI cybersecurity practices against traditional cybersecurity standards.
Adversarial Attacks Hands-On
Exploring adversarial examples and perturbation methods: FGSM, PGD, DeepFool.
Differentiating between white-box and black-box attack scenarios.
Analyzing model inversion and membership inference attacks.
Addressing data poisoning and backdoor injection during training.
Mitigating prompt injection, jailbreaking, and tool misuse in LLM-based agents.
Defensive Techniques and Model Hardening
Implementing adversarial training and data augmentation strategies.
Utilizing defensive distillation and other robustness techniques.
Applying input preprocessing, gradient masking, and regularization.
Incorporating differential privacy, noise injection, and privacy budgets.
Enabling federated learning and secure aggregation for distributed training.
Hands-On with the Adversarial Robustness Toolbox
Simulating attacks against the multi-modal agent constructed on Day 2.
Measuring robustness under perturbation and quantifying performance degradation.
Iteratively applying defenses and re-evaluating attack success rates.
Stress-testing tool-call pathways and prompt injection vectors.
Day 4
Risk Management Frameworks for AI
Implementing the NIST AI Risk Management Framework: govern, map, measure, manage.
Navigating ISO/IEC 42001 and emerging AI-specific standards.
Mapping AI risk to existing enterprise GRC frameworks.
Meeting AI accountability, auditability, and documentation requirements.
Regulatory Compliance for Agentic Systems
Navigating the EU AI Act: risk tiers, prohibited uses, and obligations for high-risk systems.
Understanding GDPR and CCPA implications for agent data pipelines.
Aligning with the U.S. Executive Order on Safe, Secure, and Trustworthy AI.
Adhering to sector-specific guidance for finance, healthcare, and public services.
Managing third-party risk and supplier AI tool usage.
Ethics, Bias, and Explainability
Detecting and mitigating bias across agent perception and reasoning.
Treating explainability and transparency as critical security properties.
Ensuring fairness, preventing downstream harm, and deploying responsibly.
Designing inclusive, auditable agent behaviors.
Production Deployment, Monitoring, and Incident Response
Implementing secure deployment patterns for single and multi-agent systems.
Establishing continuous monitoring for drift, anomalies, and abuse.
Maintaining logging, audit trails, and forensic readiness for agent actions.
Utilizing AI security incident response playbooks for recovery.
Analyzing case studies of real-world AI breaches and lessons learned.
Capstone and Synthesis
Reviewing the multi-modal multi-agent system developed throughout the course.
Evaluating the end-to-end pipeline: design, build, secure, govern, deploy.
Conducting a self-assessment of the system against NIST AI RMF functions.
Discussing forward-looking trends in agentic AI and AI security.
Summary and Next Steps
Requirements
Targeted Audience
This course is designed for AI engineers and architects developing agentic systems for production environments. It also suits cybersecurity, risk, and compliance professionals responsible for AI assurance in regulated sectors such as finance, healthcare, and consulting. Additionally, it is ideal for senior developers and solution leads who are embedding multi-modal and multi-agent capabilities into enterprise platforms.
Testimonials (3)
The trainer is patient and very helpful. He knows the topic well.
CLIFFORD TABARES - Universal Leaf Philippines, Inc.
Course - Agentic AI for Business Automation: Use Cases & Integration
Good mixvof knowledge and practice
Ion Mironescu - Facultatea S.A.I.A.P.M.
Course - Agentic AI for Enterprise Applications
The mix of theory and practice and of high level and low level perspectives
