Course Outline
Day 1
Anatomy of a Modern AI Agent
Exploring agents as autonomous reasoning and acting systems beyond traditional chatbots
Understanding 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
Analyzing LangChain, LlamaIndex, AutoGen, and CrewAI, including their respective tradeoffs
Comparing modern frameworks with classical approaches like JADE and SPADE
Selecting the appropriate framework based on production requirements
Mastering 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
Understanding FIPA ACL, message-passing, and their modern equivalents
Examining coordination patterns: planning, negotiation, and synchronization
Studying 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 trust among coordinating agents
Day 2
Multi-Modal Foundations for Agents
Viewing multi-modal AI as a unified workflow across text, image, speech, and video
Reviewing leading multi-modal models: GPT-4 Vision, Gemini, Claude, and Whisper
Examining fusion techniques for combining modalities within an agent's reasoning loop
Assessing latency, cost, and accuracy tradeoffs in multi-modal pipelines
Building the Perception Layer
Implementing image processing for agents: classification, captioning, and object detection
Utilizing Whisper ASR for speech recognition and streaming transcription
Employing text-to-speech synthesis for 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
Integrating GPT-4 Vision and Whisper APIs end-to-end
Implementing memory, state, and conversation management
Adding tool calls that produce safe, 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
Identifying why agentic AI is uniquely vulnerable compared to traditional software
Mapping the attack surface across 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 frameworks
Adversarial Attacks Hands-On
Exploring adversarial examples and perturbation methods: FGSM, PGD, DeepFool
Analyzing white-box versus black-box attack scenarios
Understanding model inversion and membership inference attacks
Addressing data poisoning and backdoor injection during training
Managing 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
Employing differential privacy, noise injection, and privacy budgets
Using 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
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
Understanding AI accountability, auditability, and documentation requirements
Regulatory Compliance for Agentic Systems
Exploring 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
Examining sector-specific guidance for finance, healthcare, and public services
Managing third-party risk and supplier AI tool usage
Ethics, Bias, and Explainability
Identifying and mitigating bias across agent perception and reasoning
Recognizing explainability and transparency as security-relevant properties
Ensuring fairness, minimizing downstream harm, and promoting responsible deployment
Designing inclusive and auditable agent behavior
Production Deployment, Monitoring, and Incident Response
Adopting secure deployment patterns for single and multi-agent systems
Implementing continuous monitoring for drift, anomalies, and abuse
Establishing logging, audit trails, and forensic readiness for agent actions
Developing 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 across the course
Conducting an end-to-end pipeline review: design, build, secure, govern, deploy
Self-assessing the system against NIST AI RMF functions
Gaining a 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
