Architecting Agentic AI: Building Autonomous and Adaptive Systems

Learn to design, build, and deploy intelligent AI agents that don’t just respond—but act, adapt, and collaborate. This course takes participants beyond single-model generative AI into the world of Agentic AI, where memory, reasoning, and orchestration allow systems to operate with greater autonomy. Through hands-on labs, design challenges, and case studies, learners gain the skills to architect secure, resilient, and effective multi-agent systems for real-world environments.

3 Days

Custom

· Software Developers interested in building advanced AI-powered systems.

· Data Scientists aiming to push beyond single-model AI solutions.

· Technical Leads exploring multi-agent orchestration for enterprise applications.

Prerequisites:

· Proficiency with Python and developer environments.

· Familiarity with GenAI fundamentals, prompt engineering, retrieval-augmented generation, and intermediate-level AI design (e.g., from Enhancing Generative AI with Retrieval Augmented Generation or equivalent).

By the end of this course, participants will be able to:

· Distinguish Agentic AI from traditional GenAI applications and articulate its advantages.

· Apply design patterns to architect robust Agentic systems.

· Implement memory and context management to create adaptive agents.

· Enable secure tool use and function calling for specialized tasks.

· Orchestrate multi-agent systems that cooperate and resolve conflicts.

· Apply planning and reasoning frameworks to complex problems.

· Design, test, and deploy production-ready Agentic AI solutions with safety and security in mind.

Of course, Susie! Here’s your Agentic AI Training Outline with all italics removed and formatting kept clean and consistent:

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Agentic AI Training Outline

Module 1: The World of Agentic AI

• What makes AI “agentic”?
• Core components: autonomy, adaptability, and orchestration
• Agentic AI vs. traditional GenAI vs. classical AI
• Real-world case studies: finance, supply chain, robotics, cybersecurity
• Lab: Deploying your first simple agent

Module 2: Architectures and Frameworks

• Agentic design patterns and trade-offs
• Popular frameworks (LangChain, AutoGen, CrewAI, etc.)
• Hybrid approaches: symbolic + generative methods
• Lab: Compare two architectures and deploy a working prototype

Module 3: Memory and Context Management

• Why memory matters for adaptability
• Types of memory: short-term, episodic, and long-term
• Implementation in frameworks (e.g., Autogen memory manager)
• Best practices and pitfalls
• Lab: Build an agent with short- and long-term memory

Module 4: Tool Use and Function Calling

• From reactive to proactive agents: the OODA Loop (Observe–Orient–Decide–Act)
• Tools vs. APIs: when and how to integrate external capabilities
• Safe input/output validation
• Lab: Give an agent access to tools (search, calculator, data API)

Module 5: Multi-Agent Orchestration

• Core principles of multi-agent systems
• Communication, cooperation, and coordination methods
• Conflict resolution and consensus-building strategies
• Case study: Customer support swarm of agents
• Lab: Build a two-agent system that negotiates and completes tasks collaboratively

Module 6: Planning and Reasoning

• Reactive vs. deliberate planning approaches
• Hierarchical task decomposition
• Symbolic + neural reasoning methods
• Lab: Implement a reasoning loop for a research assistant agent

Module 7: Learning and Adaptation

• How agents self-improve through feedback loops
• Mechanisms for continual learning and transfer learning
• Adaptation strategies in dynamic environments
• Lab: Create an adaptive agent that improves task success over iterations

Module 8: Deployment and Scaling

• Design principles for real-world applications
• From prototype to production: deployment pipelines
• Scaling challenges in distributed systems
• Lab: Deploy a scalable agent-based application on the cloud

Module 9: Security, Safety, and Robustness

• Threat models for agentic systems (prompt injection, adversarial attacks)
• Guardrails for responsible autonomy
• Sharing responsibility across agent ecosystems
• Governance and monitoring strategies
• Lab: Harden an agent against malicious inputs

Module 10: Capstone – Building Your Agentic AI Application

• Learners design, implement, and present a complete Agentic AI system that:
o Uses memory and tools
o Coordinates multiple agents
o Applies security best practices
• Peer review and instructor feedback.