Course Outline
Day 1
Anatomy of a Modern AI Agent
Moving beyond chatbots: Understanding agents as systems for autonomous reasoning and action.
Exploring reactive, proactive, hybrid, and goal-directed agent paradigms.
Identifying core components: perception, planning, memory, tool use, 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 tradeoffs.
Comparing these 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 tool calls.
Multi-Agent System Architectures
Examining centralized, decentralized, hybrid, and layered MAS designs.
Exploring FIPA ACL, message-passing mechanisms, and their modern equivalents.
Understanding 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.
Facilitating transfer learning and knowledge sharing across agents.
Managing conflict resolution and building trust among coordinating agents.
Day 2
Multi-Modal Foundations for Agents
Understanding multi-modal AI as a unified workflow spanning text, image, speech, and video.
Reviewing leading multi-modal models: GPT-4 Vision, Gemini, Claude, and Whisper.
Applying fusion techniques to combine modalities within an agent's reasoning loop.
Balancing latency, cost, and accuracy tradeoffs in multi-modal pipelines.
Building the Perception Layer
Implementing image processing for agents: classification, captioning, and object detection.
Utilizing Whisper ASR for speech recognition and streaming transcription.
Enabling 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.
Wiring up GPT-4 Vision and Whisper APIs end-to-end.
Implementing memory, state management, and conversation handling.
Adding safe 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 in a simulated environment.
Logging agent reasoning, tool calls, and decisions for inspection and audit.
Day 3
Threat Surface of Production AI Agents
Analyzing what makes agentic AI uniquely vulnerable 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 with traditional cybersecurity methodologies.
Adversarial Attacks Hands-On
Exploring adversarial examples and perturbation methods: FGSM, PGD, DeepFool.
Differentiating between white-box and black-box attack scenarios.
Understanding 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.
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 built 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 risk to existing enterprise GRC frameworks.
Addressing AI accountability, auditability, and documentation requirements.
Regulatory Compliance for Agentic Systems
Exploring the EU AI Act: risk tiers, prohibited uses, and obligations for high-risk systems.
Analyzing GDPR and CCPA implications for agent data pipelines.
Reviewing the U.S. Executive Order on Safe, Secure, and Trustworthy AI.
Examining 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.
Recognizing explainability and transparency as security-relevant properties.
Ensuring fairness, preventing downstream harm, and promoting responsible deployment.
Designing inclusive, auditable agent behavior.
Production Deployment, Monitoring, and Incident Response
Implementing secure deployment patterns for single and multi-agent systems.
Continuous monitoring for drift, anomalies, and abuse.
Establishing logging, audit trails, and forensic readiness for agent actions.
Developing AI security incident response playbooks and recovery strategies.
Analyzing case studies of real-world AI breaches and lessons learned.
Capstone and Synthesis
Reviewing the multi-modal multi-agent system built across the course.
Conducting an end-to-end pipeline review: design, build, secure, govern, deploy.
Self-assessing 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
This course is designed for AI engineers and architects developing agentic systems for production environments. It also suits cybersecurity, risk, and compliance professionals responsible for AI assurance in regulated sectors such as finance, healthcare, and consulting. Additionally, it targets 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
