Course Outline
Day 1
Anatomy of a Modern AI Agent
Beyond chatbots: agents as systems for autonomous reasoning and action
Paradigms of reactive, proactive, hybrid, and goal-directed agents
Core components: perception, planning, memory, tool utilization, and action
Design tradeoffs between single-agent and multi-agent architectures
Agent Frameworks and the Modern Stack
Overview of LangChain, LlamaIndex, AutoGen, and CrewAI, including their respective tradeoffs
Comparison with classical frameworks such as JADE and SPADE
Selecting a framework based on specific production requirements
Utilization of tool calling, function calling, and structured outputs
Hands-on: scaffolding a single Python agent equipped with tool calls
Multi-Agent System Architectures
Design patterns for centralized, decentralized, hybrid, and layered Multi-Agent Systems (MAS)
FIPA ACL, message-passing mechanisms, and their modern equivalents
Coordination patterns including planning, negotiation, and synchronization
Emergent behavior and self-organization within agent populations
Decision-Making and Learning in Agents
Application of game theory to cooperative and competitive agent interactions
Reinforcement learning within multi-agent environments
Transfer learning and knowledge sharing across different agents
Conflict resolution mechanisms and trust establishment among coordinating agents
Day 2
Multi-Modal Foundations for Agents
Multi-modal AI enabling unified workflows across text, image, speech, and video
Leading multi-modal models: GPT-4 Vision, Gemini, Claude, and Whisper
Fusion techniques for integrating modalities within an agent's reasoning loop
Tradeoffs regarding latency, cost, and accuracy in multi-modal pipelines
Building the Perception Layer
Image processing capabilities for agents: classification, captioning, and object detection
Speech recognition using Whisper ASR and streaming transcription
Text-to-speech synthesis and natural voice interaction integration
Connecting perception outputs to LLM-driven reasoning and tool selection processes
Hands-On - Building a Multi-Modal Agent in Python
Defining the agent's task, context window, and available tool inventory
End-to-end integration of GPT-4 Vision and Whisper APIs
Implementation of memory, state management, and conversation handling
Incorporating 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
Resource allocation and coordination within a simulated environment
Logging agent reasoning, tool calls, and decisions for inspection and audit purposes
Day 3
Threat Surface of Production AI Agents
Understanding why agentic AI presents unique vulnerabilities compared to traditional software
Attack surface analysis: data, model, prompt, tool, output, and interface layers
Threat modeling for agent-based systems with autonomous tool usage capabilities
Comparing AI cybersecurity practices against traditional cybersecurity standards
Adversarial Attacks Hands-On
Adversarial examples and perturbation methods: FGSM, PGD, DeepFool
Differences between white-box and black-box attack scenarios
Model inversion and membership inference attacks
Data poisoning and backdoor injection risks during training phases
Risks of prompt injection, jailbreaking, and tool misuse in LLM-based agents
Defensive Techniques and Model Hardening
Adversarial training strategies and data augmentation techniques
Defensive distillation and other robustness enhancement methods
Input preprocessing, gradient masking, and regularization approaches
Differential privacy, noise injection mechanisms, and privacy budgets
Federated learning and secure aggregation for distributed training environments
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
Applying defenses iteratively and re-evaluating attack success rates
Stress-testing tool-call pathways and identifying 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 risks 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
Implications of GDPR and CCPA for agent data pipelines
Overview of the 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 strategies across agent perception and reasoning
The role of explainability and transparency as critical security properties
Fairness, potential for downstream harm, and principles of responsible deployment
Designing inclusive and auditable agent behaviors
Production Deployment, Monitoring, and Incident Response
Secure deployment patterns for single and multi-agent systems
Continuous monitoring for drift, anomalies, and potential abuse
Logging, audit trails, and forensic readiness for agent actions
AI security incident response playbooks and recovery procedures
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-looking perspective 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 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
