Are your teams ready to build AI agents that can truly act on your systems, not just chat with users?
While agents are becoming a foundational capability for GenAI-powered automation, they can turn brittle, opaque, and hard to scale if teams don’t understand tools, memory, and execution patterns.
To win, your GenAI solutions need to run on agents that can safely use tools, manage stateful memory, and execute reliable reasoning-action loops.
The Challenge
Without a strong approach to core agent concepts, teams struggle to:
- Experiment beyond basic chatbots - Architecture decisions stay fuzzy and agents behave like thin wrappers over prompts.
- Implement robust tools and memory - One-off hacks for APIs, context, and state create brittle, unmaintainable designs.
- Explain and debug agent behavior - Reasoning-action loops are opaque, slowing adoption and blocking production workflows.
Agent capability gaps will slow automation, increase implementation risk, and keep your assistants stuck at demo stage.
Our Solution
In this hands-on workshop, your team explores and implements the foundational building blocks of AI agents using curated notebooks, sample tools, and guided labs. Areas of focus include:
- Connecting LLMs to Tools — Configure agents to call APIs, services, and internal systems safely and reliably.
- Context & State Management — Track short-term context and maintain state across multi-step interactions.
- ReAct Execution Pattern — Implement reasoning-action loops that keep agent behavior interpretable and controllable.
- Interactive Labs & Notebooks — Experiment with evolving agent behavior in Jupyter-style environments.
- Capstone & Live Coaching — Assemble a working agent and refine its design with expert feedback and debugging support.
Skills You'll Gain
- Shared Agent Vocabulary — Align your team on concepts like tools, memory, state, and ReAct execution patterns.
- Working Agent Prototypes — Build agents that combine external tools, short-term memory, and control logic on real tasks.
- Faster Path to Automation — Move from experiments to meaningful agent-powered workflows with less trial and error.
- Lower Implementation Risk — Apply proven patterns that reduce brittleness and make agents easier to explain and debug.
- Readiness for Advanced Architectures — Establish foundations for scaling into more complex, autonomous agent systems.
Who Should Attend:
Solution Essentials
Format
Virtual or in-person
Duration
4 Hours
Skill Level
Comfort with basic Python or similar scripting language recommended
Tools
Jupyter-style notebooks or equivalent environment with access to LLM and tool APIs
Explore the Remaining Agents Foundations Certification Workshops
Help your teams responsibly adopt and scale Agentic AI. Click below to explore the remaining workshops in the Agents Foundations certification series.
Advanced Concepts of AI Agents Workshop
Selecting Your Agent Architecture Workshop
Curating Your Agent Data Workshop