Course Outline
Day 1
Anatomy of a Modern AI Agent
Beyond simple chatbots: agents as systems for autonomous reasoning and action
Understanding reactive, proactive, hybrid, and goal-directed agent paradigms
Essential components: perception, planning, memory, tool usage, and action
Evaluating design tradeoffs between single-agent and multi-agent approaches
Agent Frameworks and the Modern Stack
Comparing LangChain, LlamaIndex, AutoGen, and CrewAI along with their respective tradeoffs
Contrasting modern tools with classical frameworks like JADE and SPADE
Selecting the appropriate framework based on production requirements
Understanding tool calling, function calling, and structured outputs
Hands-on: scaffolding a single Python agent with integrated tool calls
Multi-Agent System Architectures
Exploring centralized, decentralized, hybrid, and layered Multi-Agent System (MAS) designs
FIPA ACL, message-passing mechanisms, and their modern equivalents
Coordination patterns including planning, negotiation, and synchronization
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 within multi-agent environments
Facilitating transfer learning and knowledge sharing across agents
Resolving conflicts and establishing trust among coordinating agents
Day 2
Multi-Modal Foundations for Agents
Viewing multi-modal AI as a unified workflow spanning text, image, speech, and video
Examining leading multi-modal models: GPT-4 Vision, Gemini, Claude, Whisper
Techniques for fusing multiple modalities within an agent's reasoning loop
Balancing latency, cost, and accuracy in multi-modal pipelines
Building the Perception Layer
Image processing techniques for agents: classification, captioning, and object detection
Speech recognition using Whisper ASR and streaming transcription
Text-to-speech synthesis for natural voice interaction
Integrating perception outputs into LLM-driven reasoning and tool selection
Hands-On - Building a Multi-Modal Agent in Python
Defining the agent's task, context window, and available tools
End-to-end integration of GPT-4 Vision and Whisper APIs
Implementing memory management, state handling, and conversation flows
Safely adding tool calls that result in 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 purposes
Day 3
Threat Surface of Production AI Agents
Understanding why agentic AI faces unique vulnerabilities compared to traditional software
Mapping 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 against traditional cybersecurity standards
Adversarial Attacks Hands-On
Exploring adversarial examples and perturbation methods: FGSM, PGD, DeepFool
Distinguishing between white-box and black-box attack scenarios
Analyzing model inversion and membership inference attacks
Addressing data poisoning and backdoor injection during training
Mitigating 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 methods
Incorporating differential privacy, noise injection, and privacy budgets
Enabling federated learning and secure aggregation for distributed training
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
Iteratively applying defenses 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
Meeting requirements for AI accountability, auditability, and documentation
Regulatory Compliance for Agentic Systems
Navigating the EU AI Act: risk tiers, prohibited uses, and obligations for high-risk systems
Assessing GDPR and CCPA implications for agent data pipelines
Aligning with the U.S. Executive Order on Safe, Secure, and Trustworthy AI
Reviewing sector-specific guidance for finance, healthcare, and public services
Evaluating third-party risks and supplier AI tool usage
Ethics, Bias, and Explainability
Detecting and mitigating bias across agent perception and reasoning processes
Recognizing explainability and transparency as critical security properties
Ensuring fairness, preventing downstream harm, and promoting responsible deployment
Designing inclusive and auditable agent behaviors
Production Deployment, Monitoring, and Incident Response
Implementing secure deployment patterns for single and multi-agent systems
Establishing continuous monitoring for drift, anomalies, and abuse
Maintaining logs, 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 key lessons learned
Capstone and Synthesis
Reviewing the multi-modal multi-agent system built throughout the course
Evaluating the end-to-end pipeline: design, build, secure, govern, deploy
Conducting a self-assessment of the system against NIST AI RMF functions
Discussing future outlooks 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 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
