Course Outline
Day 1
Anatomy of a Modern AI Agent
Beyond chatbots: Understanding agents as systems for autonomous reasoning and action
Agent paradigms: Reactive, proactive, hybrid, and goal-directed approaches
Core components: Perception, planning, memory, tool utilization, and action
Design trade-offs between single-agent and multi-agent architectures
Agent Frameworks and the Modern Stack
Examining LangChain, LlamaIndex, AutoGen, and CrewAI, including their respective trade-offs
Comparing modern frameworks with classical solutions like JADE and SPADE
Selecting the appropriate framework based on production requirements
Techniques for tool calling, function calling, and generating structured outputs
Hands-on: Scaffolding a single Python agent with tool integration
Multi-Agent System Architectures
Architectural designs: Centralized, decentralized, hybrid, and layered MAS models
Communication standards: FIPA ACL, message-passing mechanisms, and modern equivalents
Coordination patterns: Planning, negotiation, and synchronization strategies
Understanding 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 establishing trust among coordinating agents
Day 2
Multi-Modal Foundations for Agents
Multi-modal AI as a unified workflow encompassing 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
Balancing latency, cost, and accuracy 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 interactions
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
Safely adding 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
Unique vulnerabilities of agentic AI compared to traditional software
Attack surface analysis: Data, model, prompt, tool, output, and interface layers
Threat modeling for agent-based systems with autonomous tool use
Comparing AI cybersecurity practices with traditional cybersecurity 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 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
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 critical security properties
Ensuring fairness, preventing downstream harm, and promoting responsible deployment
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 procedures
Case studies of real-world AI breaches and key 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 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
