Agent-Aware Job Scheduling

Most distributed systems were built for static workloads - batch jobs, simple queries, linear processing. But AI agents are different. They think, respond, remember, and evolve.

Hymx supports agent-native workloads such as:

• LLM-based chain-of-thought reasoning

• Autonomous decision loops with recursive outputs

• AI pipelines that require intermediate context and feedback

Tasks can be broken down into discrete segments, routed to multiple nodes, and reassembled - enabling collaborative execution at scale. This is critical for real-world AI systems where agents must work continuously, not in snapshots.