Are your GenAI assistants running on an agent architecture that will hold up under real production load?
Modern copilots depend on networks of agents, orchestration frameworks, and clear communication patterns that can quickly become brittle and hard to debug if they aren’t designed for scale.
To win, your GenAI solutions need to run on an agent architecture with clear, observable orchestration and communication patterns.
The Challenge
Without a strong approach to agent architecture, teams struggle to:
- Choosing frameworks — Compare LangGraph, Agents SDKs, and internal options without a consistent decision framework.
- Managing complexity — Bolt on state, retries, and routing logic ad hoc, leading to brittle flows and cascading failures.
- Ensuring observability — Debug opaque multi-agent runs with limited tracing, metrics, or repeatable test harnesses.
Weak agent architectures will drive quality issues, cascading failures, and slow delivery of new GenAI capabilities.
Our Solution
In this hands-on workshop, your team designs and validates an agent architecture by implementing the same flows in multiple frameworks and defining production-ready communication protocols, backed by a working test harness. Areas of focus include:
- Interactive Projects & Labs — Implement the same multi-agent flow in LangGraph and an Agents SDK using curated projects.
- Orchestration Framework Comparison — Evaluate state handling, retries, sub-graphs, and tooling fit across frameworks.
- Agent Communication Protocols — Design schemas, routing rules, timeouts, and error semantics for multi-agent workflows.
- Observability & Debugging — Integrate tracing, metrics, and logs so teams can inspect runs, find failures, and tune behavior.
- Reference Architecture & Test Harness — Produce a documented architecture and executable harness your teams can extend.
Skills You'll Gain
- Architecture Decision Patterns — Make clear, defensible choices between orchestration frameworks for your stack.
- Reliability & Resilience Design — Reduce stalls, cascades, and silent failures through explicit state, retry, and timeout strategies.
- Production-Grade Observability — Instrument agent flows with traces and metrics that support fast debugging and tuning.
- Reusable Protocol & Routing Patterns — Reapply message schemas and routing rules across agent-based products and platforms.
- Faster Path to Production — Move from experiments to maintainable, scalable agent ecosystems with less rework and risk.
Who Should Attend:
Solution Essentials
Format
Virtual or in-person
Duration
4 Hours
Skill Level
Intermediate engineers familiar with modern backend or GenAI concepts
Tools
LangGraph, an Agents SDK, and observability tooling in a curated environment
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
Advanced Concepts of AI Agents
Curating Your Agent Data