Course Outline
Day 1
Anatomy of a Modern AI Agent
Exploring agents as autonomous reasoning and acting systems beyond standard chatbots.
Examining reactive, proactive, hybrid, and goal-directed agent paradigms.
Identifying core components: perception, planning, memory, tool use, and action.
Weighing the design tradeoffs between single-agent and multi-agent architectures.
Agent Frameworks and the Modern Stack
Analyzing LangChain, LlamaIndex, AutoGen, and CrewAI, along with their respective tradeoffs.
Comparing these with classical frameworks such as JADE and SPADE.
Selecting a framework based on specific production requirements.
Understanding tool calling, function calling, and structured outputs.
Hands-on: Scaffolding a single Python agent with tool calls.
Multi-Agent System Architectures
Exploring centralized, decentralized, hybrid, and layered MAS designs.
Reviewing FIPA ACL, message-passing, and their modern equivalents.
Identifying coordination patterns: planning, negotiation, and synchronization.
Understanding emergent behaviour and self-organization in 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 between coordinating agents.
Day 2
Multi-Modal Foundations for Agents
Viewing multi-modal AI as a unified workflow encompassing text, image, speech, and video.
Examining leading multi-modal models: GPT-4 Vision, Gemini, Claude, and Whisper.
Utilizing fusion techniques to combine modalities within an agent's reasoning loop.
Evaluating latency, cost, and accuracy tradeoffs in multi-modal pipelines.
Building the Perception Layer
Implementing image processing for agents: classification, captioning, and object detection.
Utilizing speech recognition with Whisper ASR and streaming transcription.
Enabling text-to-speech synthesis and 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.
Wiring up GPT-4 Vision and Whisper APIs end-to-end.
Implementing memory, state, and conversation management.
Adding tool calls that produce real-world side effects safely.
Hands-On - Orchestrating a Multi-Agent System
Composing specialized agents with AutoGen or CrewAI.
Defining roles, responsibilities, and inter-agent communication protocols.
Managing resource allocation and coordination in a simulated environment.
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.
Mapping 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 to traditional cybersecurity approaches.
Adversarial Attacks Hands-On
Exploring adversarial examples and perturbation methods: FGSM, PGD, DeepFool.
Simulating white-box versus black-box attack scenarios.
Understanding model inversion and membership inference attacks.
Addressing data poisoning and backdoor injection during training.
Handling 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.
Employing federated learning and secure aggregation for distributed training.
Hands-On with the Adversarial Robustness Toolbox
Simulating attacks against the multi-modal agent built on Day 2.
Measuring robustness under perturbation and quantifying degradation.
Applying defenses iteratively and re-evaluating attack success rates.
Stress-testing tool-call pathways and prompt injection vectors.
Day 4
Risk Management Frameworks for AI
Navigating the NIST AI Risk Management Framework: govern, map, measure, manage.
Reviewing ISO/IEC 42001 and emerging AI-specific standards.
Mapping AI risk to existing enterprise GRC frameworks.
Addressing AI accountability, auditability, and documentation requirements.
Regulatory Compliance for Agentic Systems
Understanding the EU AI Act: risk tiers, prohibited uses, and obligations for high-risk systems.
Assessing GDPR and CCPA implications for agent data pipelines.
Reviewing the U.S. Executive Order on Safe, Secure, and Trustworthy AI.
Consulting sector-specific guidance for finance, healthcare, and public services.
Evaluating third-party risk and supplier AI tool usage.
Ethics, Bias, and Explainability
Detecting and mitigating bias across agent perception and reasoning.
Recognizing explainability and transparency as security-relevant properties.
Ensuring fairness, minimizing downstream harm, and enabling responsible deployment.
Designing inclusive, auditable agent behaviour.
Production Deployment, Monitoring, and Incident Response
Implementing secure deployment patterns for single and multi-agent systems.
Establishing continuous monitoring for drift, anomalies, and abuse.
Managing logging, audit trails, and forensic readiness for agent actions.
Utilizing AI security incident response playbooks and recovery procedures.
Reviewing case studies of real-world AI breaches and lessons learned.
Capstone and Synthesis
Reviewing the multi-modal multi-agent system built across the course.
Conducting an end-to-end pipeline review: design, build, secure, govern, deploy.
Performing self-assessment of the system against NIST AI RMF functions.
Discussing the forward outlook on emerging trends in agentic AI and AI security.
Summary and Next Steps
Requirements
Targeted Audience
AI engineers and architects developing agentic systems for production environments. Cybersecurity, risk, and compliance professionals tasked with AI assurance in regulated industries such as finance, healthcare, and consulting. Senior developers and solution leads integrating 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
