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
Examining core components: perception, planning, memory, tool use, and action
Evaluating design tradeoffs between single-agent and multi-agent approaches
Agent Frameworks and the Modern Stack
Analyzing LangChain, LlamaIndex, AutoGen, and CrewAI, along with their respective tradeoffs
Comparing modern frameworks with classical ones like JADE and SPADE
Guidelines for selecting a framework based on production requirements
Understanding tool calling, function calling, and structured outputs
Hands-on: Constructing a single Python agent with tool calls
Multi-Agent System Architectures
Exploring centralized, decentralized, hybrid, and layered MAS designs
Understanding FIPA ACL, message-passing protocols, and their modern equivalents
Coordination patterns including planning, negotiation, and synchronization
Observing emergent behavior and self-organization in 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
Addressing conflict resolution and establishing trust among coordinating agents
Day 2
Multi-Modal Foundations for Agents
Utilizing multi-modal AI as a unified workflow across 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
Evaluating latency, cost, and accuracy tradeoffs in multi-modal pipelines
Building the Perception Layer
Processing images for agents: classification, captioning, and object detection
Implementing speech recognition using Whisper ASR and streaming transcription
Employing text-to-speech synthesis for natural voice interactions
Linking 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 generate 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
Identifying what makes agentic AI uniquely vulnerable compared to traditional software
Analyzing attack surfaces across data, model, prompt, tool, output, and interface layers
Conducting threat modeling for agent-based systems with autonomous tool use
Contrasting AI cybersecurity practices with traditional cybersecurity measures
Adversarial Attacks Hands-On
Exploring adversarial examples and perturbation methods: FGSM, PGD, DeepFool
Assessing white-box versus black-box attack scenarios
Investigating model inversion and membership inference attacks
Understanding data poisoning and backdoor injection during training
Addressing 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
Ensuring differential privacy through noise injection and managing 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 in 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 prompt injection vectors
Day 4
Risk Management Frameworks for AI
Implementing the NIST AI Risk Management Framework: govern, map, measure, manage
Exploring ISO/IEC 42001 and emerging AI-specific standards
Mapping AI risk to existing enterprise GRC frameworks
Addressing AI accountability, auditability, and documentation requirements
Regulatory Compliance for Agentic Systems
Navigating the EU AI Act: risk tiers, prohibited uses, and obligations for high-risk systems
Understanding GDPR and CCPA implications for agent data pipelines
Reviewing 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
Establishing 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
Implementing secure deployment patterns for single and multi-agent systems
Conducting continuous monitoring for drift, anomalies, and abuse
Maintaining 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 key 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 self-assessment of the system against NIST AI RMF functions
Looking forward to emerging trends in agentic AI and AI security
Summary and Next Steps
Requirements
Target Audience
AI engineers and architects tasked with developing agentic systems for production environments. Cybersecurity, risk, and compliance experts responsible for AI assurance within regulated sectors such as finance, healthcare, and consulting. Senior developers and solution leads who are 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
