Course Outline
Day 1
Anatomy of a Modern AI Agent
Understanding agents beyond chatbots as systems for autonomous reasoning and action
Exploring reactive, proactive, hybrid, and goal-directed agent paradigms
Examining core components: perception, planning, memory, tool utilization, and action
Evaluating design trade-offs between single-agent and multi-agent architectures
Agent Frameworks and the Modern Stack
Analyzing LangChain, LlamaIndex, AutoGen, and CrewAI, including their respective trade-offs
Comparing these with classical frameworks like JADE and SPADE
Strategies for selecting frameworks based on production requirements
Understanding tool calling, function calling, and structured outputs
Hands-on: Scaffolding a single Python agent equipped with tool calls
Multi-Agent System Architectures
Exploring centralized, decentralized, hybrid, and layered MAS designs
Reviewing FIPA ACL, message-passing mechanisms, and modern equivalents
Studying coordination patterns: planning, negotiation, and synchronization
Investigating 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
Leveraging transfer learning and knowledge sharing across agents
Managing conflict resolution and trust among coordinating agents
Day 2
Multi-Modal Foundations for Agents
Integrating multi-modal AI as a unified workflow encompassing text, image, speech, and video
Examining leading multi-modal models: GPT-4 Vision, Gemini, Claude, and Whisper
Applying fusion techniques to combine modalities within an agent's reasoning loop
Assessing latency, cost, and accuracy trade-offs in multi-modal pipelines
Building the Perception Layer
Utilizing image processing for agents: classification, captioning, and object detection
Implementing speech recognition with Whisper ASR 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 management, and conversation handling
Adding tool calls that execute real-world side effects safely
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
Understanding what makes agentic AI uniquely vulnerable compared to traditional software
Identifying 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 with traditional cybersecurity measures
Adversarial Attacks Hands-On
Exploring adversarial examples and perturbation methods: FGSM, PGD, and DeepFool
Analyzing white-box versus black-box attack scenarios
Investigating model inversion and membership inference attacks
Addressing data poisoning and backdoor injection during training
Combating 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
Integrating differential privacy, noise injection, and privacy budgets
Employing 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
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
Understanding ISO/IEC 42001 and emerging AI-specific standards
Mapping AI risks to existing enterprise GRC frameworks
Meeting AI accountability, auditability, and documentation requirements
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
Understanding the U.S. Executive Order on Safe, Secure, and Trustworthy AI
Adhering to sector-specific guidance for finance, healthcare, and public services
Managing third-party risk and supplier AI tool usage
Ethics, Bias, and Explainability
Detecting and mitigating bias across agent perception and reasoning
Ensuring explainability and transparency as security-relevant properties
Promoting fairness, preventing downstream harm, and ensuring responsible deployment
Designing inclusive, auditable agent behavior
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 logging, audit trails, and forensic readiness for agent actions
Utilizing AI security incident response playbooks and recovery strategies
Reviewing case studies of real-world AI breaches and lessons learned
Capstone and Synthesis
Reviewing the multi-modal multi-agent system developed throughout the course
Conducting an end-to-end pipeline review: design, build, secure, govern, deploy
Performing a self-assessment of 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
Target 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
