Are your agents ready to scale beyond simple tasks into reliable, multi-step copilots?
Modern AI stacks depend on agents that can coordinate multiple tools, run work in parallel, and remember users over time without becoming brittle or impossible to debug.
To win, your GenAI solutions need to run on scalable, memory-aware agent architectures you can reason about and trust.
The Challenge
Without a strong approach to advanced agent design, teams struggle to:
- Scale beyond simple flows - Agents remain single-threaded, slow, and unable to juggle real-world workloads.
- Structure modular logic - Spaghetti graphs and tightly coupled steps make change risky and debugging painful.
- Manage persistent memory - Context is lost between tasks and sessions, undermining personalization and reliability.
Agent design gaps will drive wasted compute, unpredictable behavior, and user experiences that never feel truly production ready.
Our Solution
In this hands-on workshop, your team designs, implements, and validates scalable LangGraph-based agents with parallel sub-graphs and persistent memory. Areas of focus include:
- Parallel LangGraph Patterns - Implement parallel branches and sub-graphs that handle multiple tasks concurrently.
- Modular Sub-Graph Design - Break complex workflows into reusable, composable LangGraph components.
- Practical Memory Architectures - Use LangGraph Store to implement short-term, long-term, and user-specific memory.
- Interactive Labs & Tracing - Visualize LangGraph flows and execution traces in curated notebooks.
- Capstone & Live Coaching - Assemble a production-minded agent graph with expert guidance and review.
Skills You'll Gain
- Scalable Agent Architectures - Move from linear toy examples to robust, parallel agent workflows.
- Persistent Personalization - Use structured memory so agents remember user preferences and histories over time.
- Reliable Multi-Step Execution - Reduce brittle behavior with clear, testable sub-graphs and memory layers.
- Faster Agent Development - Apply reusable patterns to build, extend, and debug complex agents faster.
- Production Readiness - Adapt workshop designs to real user journeys, tool stacks, and environments.
Who Should Attend:
Solution Essentials
Format
Virtual or in-person
Duration
4 Hours
Skill Level
Intermediate Python and basic LLM familiarity recommended
Tools
LangGraph, LangGraph Store, Python, Jupyter notebooks
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.
Core Concepts & Capabilities of AI Agents
Selecting Your Agent Architecture
Curating Your Agent Data