NYC | Agentic AI Workshop

This workshop is your chance to get ahead of the curve. You’ll learn how agents reason, plan, and act — and you’ll build your own working prototypes that showcase these capabilities.

Whether you’re a developer, data scientist, entrepreneur, executive, or business innovator, you’ll gain hands-on experience with the latest agentic frameworks — from Langflow and CrewAI to retrieval and memory systems that power autonomous behavior.

No heavy coding required — just curiosity, creativity, and a desire to explore what’s next.

Don’t just read about the next big leap in AI — build it.

• Ticket prices and availability are subject to increase or decrease, at the discretion of ODSC AI, before and/or after you have made your purchase, and do not entitle the purchaser to a partial refund or credit.
• Given the nature of live events, changes to specific sessions and/or presenters may be unavoidable and do not entitle the purchaser to a partial (or full) refund or credit. ODSC AI reserves the right to expel and/or void the Ticket of any attendee or participant who violates the .

Info: Introductions and overview of the 2-day workshop. What is Agentic AI?

Objectives and outcomes of the workshop and hackathon

Info: Quick intro to agentic AI and Langflow interface.

Build a basic agent: LLM + tool + feedback loop.

Run and observe your first autonomous workflow.

Info: Overview of Large Language Models (LLMs).

Prompt engineering, reasoning, and Chain-of-Thought (CoT).

Introduction to ReAct prompting and reasoning loops.

How LLMs enable agentic reasoning.

Info: Extend your Langflow agent with reasoning.

Add tool use and structured task execution.

Info: Components: planning, memory, tools, and environment.

Frameworks overview: LangChain, CrewAI, LlamaIndex.

The Agent Loop: perception → reasoning → action → feedback.

Examples of orchestration and coordination.

Info: Short-term vs. long-term memory in agents.

How RAG extends memory through retrieval.

Integrating vector databases and embeddings.

Demo: augmenting agent responses with external knowledge.

Info: Multi-agent collaboration patterns (planner, critic, executor).

Using CrewAI to coordinate roles and goals.

Designing modular, role-based workflows.

Preparing agents for real-world applications.

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