Course Outline
Day 1
Anatomy of a Modern AI Agent
Understanding agents as autonomous reasoning and acting systems, going beyond traditional chatbots
Exploring reactive, proactive, hybrid, and goal-directed agent paradigms
Identifying core components: perception, planning, memory, tool use, and action
Evaluating tradeoffs between single-agent and multi-agent designs
Agent Frameworks and the Modern Stack
Examining LangChain, LlamaIndex, AutoGen, CrewAI, and their respective tradeoffs
Comparing modern frameworks with classical ones like JADE and SPADE
Selecting the right framework based on production requirements
Understanding tool calling, function calling, and structured outputs
Hands-on: Scaffolding a single Python agent with tool calls
Multi-Agent System Architectures
Designing centralized, decentralized, hybrid, and layered MAS structures
Studying FIPA ACL, message-passing, and modern equivalents
Mastering coordination patterns: planning, negotiation, and synchronization
Exploring emergent behavior 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
Leveraging transfer learning and knowledge sharing across agents
Resolving conflicts and building trust between coordinating agents
Day 2
Multi-Modal Foundations for Agents
Integrating multi-modal AI as a unified workflow across text, image, speech, and video
Reviewing leading multi-modal models: GPT-4 Vision, Gemini, Claude, Whisper
Applying fusion techniques to combine modalities within an agent's reasoning loop
Balancing latency, cost, and accuracy in multi-modal pipelines
Building the Perception Layer
Processing images for agents: classification, captioning, object detection
Performing speech recognition with Whisper ASR and streaming transcription
Implementing text-to-speech synthesis and natural voice interaction
Linking 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
Connecting GPT-4 Vision and Whisper APIs end-to-end
Implementing memory, state, and conversation management
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 a simulated environment
Logging agent reasoning, tool calls, and decisions for inspection and audit
Day 3
Threat Surface of Production AI Agents
Understanding why agentic AI is 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 to traditional cybersecurity methods
Adversarial Attacks Hands-On
Executing adversarial examples and perturbation methods: FGSM, PGD, DeepFool
Exploring white-box versus black-box attack scenarios
Performing model inversion and membership inference attacks
Identifying data poisoning and backdoor injection risks during training
Addressing prompt injection, jailbreaking, and tool misuse in LLM-based agents
Defensive Techniques and Model Hardening
Implementing adversarial training and data augmentation strategies
Applying defensive distillation and other robustness techniques
Utilizing input preprocessing, gradient masking, and regularization
Enforcing 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 developed on Day 2
Measuring robustness under perturbation and quantifying performance 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
Applying 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
Meeting 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
Evaluating 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
Recognizing explainability and transparency as critical security properties
Ensuring fairness, preventing downstream harm, and deploying responsibly
Designing inclusive and auditable agent behavior
Production Deployment, Monitoring, and Incident Response
Implementing secure deployment patterns for single and multi-agent systems
Continuously monitoring for drift, anomalies, and abuse
Maintaining logging, audit trails, and forensic readiness for agent actions
Utilizing AI security incident response playbooks and recovery procedures
Analyzing case studies of real-world AI breaches and lessons learned
Capstone and Synthesis
Reviewing the multi-modal multi-agent system built throughout the course
Conducting an end-to-end pipeline review: design, build, secure, govern, deploy
Assessing the system's compliance with NIST AI RMF functions
Exploring 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 ensuring AI assurance in regulated sectors 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
