Union.ai, the leader in AI orchestration, today announced the release of a new open standard that defines a modern approach to workflow orchestration for the era of AI, ML, and agents. The new standard outlines the core principles of an orchestration framework built for today’s dynamic, long-running AI workloads, which legacy systems are not equipped to handle.
“The assumption that all AI workloads behave like static batch jobs no longer holds true,” said Ketan Umare, CEO and Co-Founder of Union.ai. “Today’s systems are non-deterministic, long-running, and require AI development infrastructure that adapts in real time to the needs of the application, instead of the other way around.”
The open standard is built upon the collective experience of the Flyte open-source community, which has been at the forefront of scaling AI workflows in production for years. The principles laid out in the standard are designed to address the most pressing challenges faced by engineers building AI systems today, including the need for dynamic execution, simplified debugging, and resilient pipeline orchestration.
The new open standard for AI orchestration is defined by the following key principles:
- Dynamic, On-the-Fly Orchestration:Â Orchestration frameworks must support real-time decision-making, conditional branching, and loops, enabling AI systems and agents to adapt at runtime.
- Fault-Tolerant Pipelines: The standard emphasizes the need for resilience and durability. Workflows should be self-healing, or able to automatically recover from interruptions and continue where they left off, with built-in caching, retries, and custom error handling to prevent failures from bringing down the entire workflow.
- Efficient, Scalable, and Infrastructure-Aware Execution: A modern orchestration system must be able to handle large task fanout and parallelism with ease. It should also allow for on-demand resource provisioning and autoscaling to optimize infrastructure costs.
- Streamlined Debugging and Observability: The standard defines the need for visibility into execution state, logs, and failures at every step. This includes the ability to catch and react to errors as they happen and rerun workflows in a live debugger.
- Compliance and Governance Guardrails: The standard promotes best practices such as automatic execution versioning, multi-tenancy support for dev, staging, and production environments, and declarative, on-demand infrastructure provisioning.
This new standard provides a clear path forward for those seeking alternatives to other legacy systems. To learn more, visit www.union.ai or join the conversation on Slack and LinkedIn.
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