Are your teams confident running complex GenAI toolchains in production when things fail, slow down, or behave unpredictably?
Runtime orchestration is the control plane for multi-step GenAI workflows, and without it, even strong tools and models turn into fragile systems that are hard to trust at scale.
To win, your GenAI solutions need a resilient orchestration layer that handles failures, protects users, and scales without constant firefighting.
The Challenge
Without a strong approach to runtime orchestration and control, teams struggle to:
- Bolt on orchestration logic as ad hoc glue code that is brittle and hard to test.
- Manage retries, timeouts, concurrency, and approvals in a consistent, debuggable way.
- Roll out new GenAI workflows safely when outages, latency spikes, and tool errors are inevitable.
Orchestration gaps will drive reliability incidents, user-facing errors, and stalled rollout of critical GenAI workflows.
Our Solution
In this hands-on workshop, your team designs, implements, and validates robust orchestration patterns for multi-tool GenAI systems using curated notebooks and example frameworks. Areas of focus include:
- Failure-First Design Mindset - Model real-world failures and bake resilience into orchestration from the start.
- Retries, Timeouts, and Circuit Breakers - Apply practical patterns to contain flakiness, latency spikes, and tool outages.
- Deterministic Concurrency and Parallelism - Run tools in parallel with clear limits, ordering rules, and rollback strategies.
- Human-in-the-Loop Controls - Add approval, interruption, and override steps for sensitive or high-impact actions.
- Observability, Runbooks, and Capstone Build - Wire in logging, metrics, alerts, and assemble a working runtime controller for a realistic GenAI workflow.
Skills You'll Gain
- Resilient Orchestration Design - Design failure-first orchestration patterns instead of ad hoc glue code.
- Runtime Reliability Patterns - Implement retries, timeouts, circuit breakers, and limits that reduce failed runs and partial outcomes.
- Safe Human-in-the-Loop Workflows - Insert approvals, escalations, and stop points for high-risk actions.
- Production Observability and Debugging - Use logs, metrics, alerts, and runbooks to debug orchestration issues quickly.
- Confident Production Rollouts - Move critical GenAI workflows from experiment to scaled production with clear controls and governance.
Who Should Attend:
Solution Essentials
Format
Virtual or in-person
Duration
4 Hours
Skill Level
Intermediate to advanced Python and GenAI experience recommended
Tools
Jupyter notebooks plus example orchestration frameworks and observability tooling
Explore the Remaining Advanced GenAI Tools Certification Workshops
Help your teams master advanced GenAI Tool concepts and solutions. Click below to explore the remaining workshops in the Advanced GenAI Tools certification series.
Monitoring, Reliability & Change Management
Explainability & Customization
MCP & Model + Tool
Co-Processing
Co-Processing
Self-Tuning / Adaptive Tool Invocation
Tool Cost & Resource Optimization