Course Outline
Day 1
Anatomy of a Modern AI Agent
Agents as autonomous reasoning and acting systems, moving beyond traditional chatbots.
Reactive, proactive, hybrid, and goal-directed agent paradigms.
Core components: perception, planning, memory, tool use, and action.
Trade-offs between single-agent and multi-agent designs.
Agent Frameworks and the Modern Stack
LangChain, LlamaIndex, AutoGen, CrewAI, and their respective trade-offs.
Comparison with classical frameworks such as JADE and SPADE.
Selecting a framework based on production requirements.
Tool calling, function calling, and structured outputs.
Hands-on: scaffolding a single Python agent with tool calls.
Multi-Agent System Architectures
Centralized, decentralized, hybrid, and layered MAS designs.
FIPA ACL, message-passing, and modern equivalents.
Coordination patterns: planning, negotiation, synchronization.
Emergent behavior and self-organization in agent populations.
Decision-Making and Learning in Agents
Game theory for cooperative and competitive agent interactions.
Reinforcement learning in multi-agent environments.
Transfer learning and knowledge sharing across agents.
Conflict resolution and trust between coordinating agents.
Day 2
Multi-Modal Foundations for Agents
Multi-modal AI as a unified workflow across text, image, speech, and video.
Leading multi-modal models: GPT-4 Vision, Gemini, Claude, Whisper.
Fusion techniques for combining modalities inside an agent's reasoning loop.
Latency, cost, and accuracy trade-offs in multi-modal pipelines.
Building the Perception Layer
Image processing for agents: classification, captioning, object detection.
Speech recognition with Whisper ASR and streaming transcription.
Text-to-speech synthesis and 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.
Wiring up GPT-4 Vision and Whisper APIs end-to-end.
Implementing memory, state, and conversation management.
Adding tool calls that produce real-world side effects safely.
Hands-On - Orchestrating a Multi-Agent System
Composing specialized agents with AutoGen or CrewAI.
Defining roles, responsibilities, and inter-agent communication protocols.
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
What makes agentic AI uniquely vulnerable compared to traditional software.
Attack surface: data, model, prompt, tool, output, and interface layers.
Threat modeling for agent-based systems with autonomous tool use.
Comparing AI cybersecurity practices to traditional cybersecurity.
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 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 risk to existing enterprise GRC frameworks.
AI accountability, auditability, and documentation requirements.
Regulatory Compliance for Agentic Systems
EU AI Act: risk tiers, prohibited uses, and obligations for high-risk systems.
GDPR and CCPA implications for agent data pipelines.
U.S. Executive Order on Safe, Secure, and Trustworthy AI.
Sector-specific guidance for finance, healthcare, and public services.
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 security-relevant properties.
Fairness, downstream harm, and responsible deployment.
Designing inclusive, 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.
Case studies of real-world AI breaches and lessons learned.
Capstone and Synthesis
Reviewing the multi-modal multi-agent system built across 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 constructing 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
