Course Outline
Day 1
Anatomy of a Modern AI Agent
Exploring agents as autonomous reasoning and acting systems, extending beyond traditional chatbots.
Examining reactive, proactive, hybrid, and goal-directed agent paradigms.
Understanding core components: perception, planning, memory, tool utilization, and action.
Evaluating design tradeoffs between single-agent and multi-agent architectures.
Agent Frameworks and the Modern Stack
Analyzing LangChain, LlamaIndex, AutoGen, and CrewAI, along with their respective advantages and limitations.
Comparing modern frameworks with classical systems like JADE and SPADE.
Selecting the appropriate framework based on specific production requirements.
Mastering tool calling, function calling, and generating structured outputs.
Hands-on activity: Building a foundational single Python agent equipped with tool calls.
Multi-Agent System Architectures
Designing centralized, decentralized, hybrid, and layered Multi-Agent System (MAS) architectures.
Understanding FIPA ACL, message-passing mechanisms, and their modern equivalents.
Implementing coordination patterns such as planning, negotiation, and synchronization.
Observing emergent behavior and self-organization within agent populations.
Decision-Making and Learning in Agents
Applying game theory to cooperative and competitive agent interactions.
Utilizing reinforcement learning in multi-agent environments.
Facilitating transfer learning and knowledge sharing across diverse agents.
Resolving conflicts and building trust among coordinating agents.
Day 2
Multi-Modal Foundations for Agents
Viewing multi-modal AI as a unified workflow encompassing text, image, speech, and video.
Exploring leading multi-modal models: GPT-4 Vision, Gemini, Claude, and Whisper.
Employing fusion techniques to integrate modalities within an agent's reasoning loop.
Balancing latency, cost, and accuracy tradeoffs in multi-modal pipelines.
Building the Perception Layer
Processing images for agents through classification, captioning, and object detection.
Implementing speech recognition using Whisper ASR and streaming transcription.
Utilizing text-to-speech synthesis for natural voice interactions.
Connecting perception outputs to LLM-driven reasoning and tool selection processes.
Hands-On - Building a Multi-Modal Agent in Python
Defining the agent's task, context window, and available tool inventory.
Integrating GPT-4 Vision and Whisper APIs end-to-end.
Implementing memory management, state tracking, and conversation handling.
Safely adding tool calls that produce 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 within a simulated environment.
Logging agent reasoning, tool calls, and decisions for inspection and auditing.
Day 3
Threat Surface of Production AI Agents
Identifying what makes agentic AI 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.
Contrasting 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.
Understanding model inversion and membership inference attacks.
Recognizing data poisoning and backdoor injection risks 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 methods.
Incorporating differential privacy, 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 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 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
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.
Following 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 processes.
Valuing 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 logs, audit trails, and forensic readiness for agent actions.
Developing AI security incident response playbooks and recovery strategies.
Studying 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.
Self-assessing the system against NIST AI RMF functions.
Gaining insight into emerging trends in agentic AI and AI security.
Summary and Next Steps
Requirements
Targeted Audience
This course is designed for AI engineers and architects developing agentic systems for production environments. It also suits cybersecurity, risk, and compliance professionals tasked with ensuring AI assurance in regulated sectors such as finance, healthcare, and consulting. Additionally, senior developers and solution leads looking to integrate multi-modal and multi-agent capabilities into enterprise platforms will find this training valuable.
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
