Hermes Agent spawns batched research swarms

Hot take: this is real infrastructure leverage, not just hype. If your tasks are batchable, the model stop-start overhead matters less than batching, isolation, and clean result aggregation.

• –Hermes Agent explicitly supports batch processing across hundreds or thousands of prompts, plus subagent delegation and `execute_code`, which maps cleanly to the orchestrator/worker pattern in the post.
• –Its code-execution path keeps intermediate tool outputs out of the main context window, reducing token bloat on multi-step research and coding workflows.
• –The 42-minute-to-70-second math only holds when prompts are similar enough to batch, the backend can sustain the throughput, and tool/runtime overhead stays low.
• –For real research workflows, the hard part becomes sandboxing, deduping outputs, and merging results, not prompting more cleverly.
• –That makes Hermes Agent more interesting as an agent infrastructure layer than as a single-chat assistant.