Course Outline
Day 1
Anatomy of a Modern AI Agent
Beyond chatbots: agents as systems for autonomous reasoning and action
Exploring reactive, proactive, hybrid, and goal-directed agent paradigms
Core components: perception, planning, memory, tool use, and action
Design tradeoffs between single-agent and multi-agent architectures
Agent Frameworks and the Modern Stack
Evaluating LangChain, LlamaIndex, AutoGen, and CrewAI, including their respective advantages and limitations
Comparing modern frameworks with classical solutions like JADE and SPADE
Selecting the appropriate framework based on production requirements
Understanding tool calling, function calling, and structured outputs
Hands-on: Building a foundational Python agent with integrated tool calls
Multi-Agent System Architectures
Designing centralized, decentralized, hybrid, and layered Multi-Agent Systems (MAS)
FIPA ACL, message-passing protocols, and their modern equivalents
Coordination patterns: planning, negotiation, and synchronization
Understanding 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 trust among coordinating agents
Day 2
Multi-Modal Foundations for Agents
Integrating multi-modal AI into unified workflows spanning text, image, speech, and video
Overview of leading multi-modal models: GPT-4 Vision, Gemini, Claude, and Whisper
Fusion techniques for combining data modalities within an agent's reasoning loop
Balancing latency, cost, and accuracy in multi-modal pipelines
Building the Perception Layer
Image processing for agents: classification, captioning, and object detection
Speech recognition using Whisper ASR and streaming transcription
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 available tools
End-to-end integration of GPT-4 Vision and Whisper APIs
Implementing memory management, state tracking, and conversation flow
Safely executing tool calls that result in 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 simulated environments
Logging agent reasoning, tool calls, and decisions for inspection and audit purposes
Day 3
Threat Surface of Production AI Agents
Understanding why agentic AI presents unique vulnerabilities compared to traditional software
Analyzing the attack surface across data, model, prompt, tool, output, and interface layers
Conducting threat modeling for agent-based systems with autonomous capabilities
Differentiating AI cybersecurity practices from traditional approaches
Adversarial Attacks Hands-On
Exploring adversarial examples and perturbation methods: FGSM, PGD, and DeepFool
Distinguishing between white-box and black-box attack scenarios
Conducting model inversion and membership inference attacks
Identifying risks of 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
Applying defensive distillation and other robustness-enhancing techniques
Utilizing input preprocessing, gradient masking, and regularization
Incorporating differential privacy, noise injection, and managing privacy budgets
Enabling federated learning and secure aggregation for distributed training
Hands-On with the Adversarial Robustness Toolbox
Simulating attacks against the multi-modal agent developed 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
Integrating AI risk into existing enterprise GRC frameworks
Meeting requirements for AI accountability, auditability, and documentation
Regulatory Compliance for Agentic Systems
EU AI Act: risk tiers, prohibited uses, and obligations for high-risk systems
Implications of GDPR and CCPA on agent data pipelines
Overview of the U.S. Executive Order on Safe, Secure, and Trustworthy AI
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 layers
Leveraging explainability and transparency as critical security properties
Ensuring fairness, minimizing downstream harm, and promoting responsible deployment
Designing inclusive and auditable agent behaviors
Production Deployment, Monitoring, and Incident Response
Implementing secure deployment patterns for single and multi-agent systems
Continuous monitoring for drift, anomalies, and malicious abuse
Maintaining logs, audit trails, and forensic readiness for agent actions
Utilizing AI security incident response playbooks and recovery procedures
Analyzing case studies of real-world AI breaches and deriving lessons learned
Capstone and Synthesis
Reviewing the multi-modal multi-agent system developed throughout the course
Evaluating the end-to-end pipeline: design, build, secure, govern, and deploy
Assessing system capabilities against NIST AI RMF functions
Exploring future trends in agentic AI and AI security
Summary and Next Steps
Requirements
Targeted Audience
This course is ideal for AI engineers and architects developing agentic systems for production environments. It also serves cybersecurity, risk, and compliance professionals responsible for AI assurance in regulated sectors such as finance, healthcare, and consulting. Additionally, senior developers and solution leads who integrate multi-modal and multi-agent capabilities into enterprise platforms will find this training highly relevant.
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
