Course Outline
Day 1
Anatomy of a Modern AI Agent
Beyond chatbots: understanding agents as autonomous reasoning and acting systems
Paradigms: reactive, proactive, hybrid, and goal-directed agent models
Core components: perception, planning, memory, tool use, and action
Tradeoffs between single-agent and multi-agent design approaches
Agent Frameworks and the Modern Stack
Evaluating LangChain, LlamaIndex, AutoGen, and CrewAI alongside their tradeoffs
Comparison with classical frameworks like JADE and SPADE
Selecting a framework based on specific production requirements
Mechanisms for 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 multi-agent system (MAS) structures
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
Establishing conflict resolution and trust among coordinating agents
Day 2
Multi-Modal Foundations for Agents
Unified workflows in multi-modal AI spanning text, image, speech, and video
Leading multi-modal models: GPT-4 Vision, Gemini, Claude, and Whisper
Fusion techniques for integrating multiple modalities within an agent's reasoning loop
Balancing latency, cost, and accuracy in multi-modal pipelines
Building the Perception Layer
Image processing capabilities: classification, captioning, and object detection
Speech recognition using Whisper ASR and streaming transcription
Text-to-speech synthesis for natural voice interactions
Integrating perception outputs with 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
Safely implementing 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
Unique vulnerabilities of agentic AI compared to traditional software
Attack surfaces: data, model, prompt, tool, output, and interface layers
Threat modeling for agent-based systems with autonomous tool use
Differentiating AI cybersecurity practices from traditional approaches
Adversarial Attacks Hands-On
Adversarial examples and perturbation methods: FGSM, PGD, DeepFool
White-box versus black-box attack scenarios
Model inversion and membership inference attacks
Data poisoning and backdoor injection during training
Prompt injection, jailbreaking, and tool misuse in LLM-based agents
Defensive Techniques and Model Hardening
Adversarial training and data augmentation strategies
Defensive distillation and other robustness techniques
Input preprocessing, gradient masking, and regularization
Differential privacy, noise injection, and privacy budgets
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 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
NIST AI Risk Management Framework: govern, map, measure, manage
ISO/IEC 42001 and emerging AI-specific standards
Mapping AI risk to existing enterprise GRC frameworks
Requirements for AI accountability, auditability, and documentation
Regulatory Compliance for Agentic Systems
EU AI Act: risk tiers, prohibited uses, and obligations for high-risk systems
GDPR and CCPA implications for agent data pipelines
U.S. Executive Order on Safe, Secure, and Trustworthy AI
Sector-specific guidance for finance, healthcare, and public services
Managing third-party risk and supplier AI tool usage
Ethics, Bias, and Explainability
Bias detection and mitigation across agent perception and reasoning
Explainability and transparency as security-relevant properties
Fairness, downstream harm, and responsible deployment practices
Designing inclusive and auditable agent behavior
Production Deployment, Monitoring, and Incident Response
Secure deployment patterns for single and multi-agent systems
Continuous monitoring for drift, anomalies, and abuse
Logging, audit trails, and forensic readiness for agent actions
AI security incident response playbooks and recovery strategies
Case studies of real-world AI breaches and lessons learned
Capstone and Synthesis
Reviewing the multi-modal multi-agent system developed throughout the course
End-to-end pipeline review: design, build, secure, govern, deploy
Self-assessment of the system against NIST AI RMF functions
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 responsible for 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
