Advanced AI Agents Masterclass: Engineering, Security, and Governance for Production Systems Training Course

Day 1

Structure of Modern AI Agents

Moving beyond chatbots to autonomous reasoning and action systems

Paradigms including reactive, proactive, hybrid, and goal-directed agents

Essential components: perception, planning, memory, tool utilization, and action

Design trade-offs between single-agent and multi-agent architectures

Agent Frameworks and the Contemporary Tech Stack

Evaluating LangChain, LlamaIndex, AutoGen, CrewAI, and their respective advantages

Comparison with legacy frameworks like JADE and SPADE

Selecting frameworks based on production requirements

Tool invocation, function calling, and structured output handling

Practical exercise: Constructing a single Python agent with tool capabilities

Multi-Agent System Architectures

Designs for centralized, decentralized, hybrid, and layered Multi-Agent Systems (MAS)

FIPA ACL, message passing mechanisms, and modern alternatives

Coordination strategies: planning, negotiation, and synchronization

Emergent behaviors and self-organization within agent populations

Decision-Making and Learning in Agents

Game theory applications for cooperative and competitive agent interactions

Reinforcement learning in multi-agent environments

Transfer learning and knowledge sharing among agents

Resolving conflicts and establishing trust between coordinating agents

Day 2

Multi-Modal Foundations for Agents

Unifying text, image, speech, and video workflows in multi-modal AI

Key multi-modal models: GPT-4 Vision, Gemini, Claude, and Whisper

Fusion techniques for integrating modalities within an agent's reasoning loop

Trade-offs regarding latency, cost, and accuracy in multi-modal pipelines

Constructing the Perception Layer

Image processing for agents: classification, captioning, and object detection

Speech recognition using Whisper ASR and streaming transcription

Text-to-speech synthesis and natural voice interaction

Linking perception outputs to LLM-driven reasoning and tool selection

Practical Exercise - Building a Multi-Modal Agent in Python

Defining the agent's tasks, context window, and available tools

Integrating GPT-4 Vision and Whisper APIs end-to-end

Implementing memory, state management, and conversation handling

Adding tool calls that safely produce real-world effects

Practical Exercise - Orchestrating a Multi-Agent System

Assembling specialized agents using 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 audit purposes

Day 3

Threat Landscape for Production AI Agents

Unique vulnerabilities of agentic AI compared to traditional software

Attack surfaces: data, model, prompt, tool, output, and interface layers

Threat modeling for agent-based systems with autonomous tool usage

Contrasting AI cybersecurity practices with traditional cybersecurity

Adversarial Attacks Practical Session

Adversarial examples and perturbation methods: FGSM, PGD, DeepFool

White-box vs. black-box attack scenarios

Model inversion and membership inference attacks

Data poisoning and backdoor injection during the training phase

Prompt injection, jailbreaking, and tool misuse in LLM-based agents

Defensive Strategies and Model Hardening

Adversarial training and data augmentation techniques

Defensive distillation and other robustness methods

Input preprocessing, gradient masking, and regularization

Differential privacy, noise injection, and privacy budget management

Federated learning and secure aggregation for distributed training

Practical Session with the Adversarial Robustness Toolbox

Simulating attacks against the multi-modal agent developed on Day 2

Measuring robustness under perturbations and quantifying performance degradation

Iteratively applying defenses and reassessing 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, and manage

ISO/IEC 42001 and emerging AI-specific standards

Aligning AI risks with existing enterprise GRC frameworks

Requirements for AI accountability, auditability, and documentation

Regulatory Compliance for Agentic Systems

EU AI Act: risk categories, prohibited uses, and high-risk system obligations

Implications of GDPR and CCPA 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 risks and the use of supplier AI tools

Ethics, Bias, and Explainability

Detecting and mitigating bias in agent perception and reasoning

Explainability and transparency as critical security attributes

Fairness, downstream impacts, and responsible deployment

Designing inclusive and auditable agent behaviors

Production Deployment, Monitoring, and Incident Response

Secure deployment patterns for single and multi-agent systems

Continuous monitoring for drift, anomalies, and misuse

Logging, audit trails, and forensic readiness for agent actions

AI security incident response playbooks and recovery strategies

Case studies of real-world AI breaches and key takeaways

Capstone Project and Synthesis

Reviewing the multi-modal multi-agent system developed throughout the course

End-to-end pipeline review: design, build, secure, govern, and deploy

Self-assessment of the system against NIST AI RMF functions

Future outlook on emerging trends in agentic AI and AI security

Summary and Next Steps