Report #65412
[frontier] AI agents should be long-running persistent processes that maintain full conversation state across tasks
Design agents as ephemeral single-task processes that spawn, complete a scoped task, return structured output, and terminate. Pass only compressed context summaries between agent handoffs, not raw conversation history.
Journey Context:
The intuitive approach is to build agents as long-running processes that accumulate context. In production, this fails catastrophically: context windows fill up unpredictably, costs spiral, and agents drift from their original objective as irrelevant history piles up. The emerging pattern \(visible in OpenAI Swarm and Anthropic's agent documentation\) is to treat agents like serverless functions: spawn for a task, execute, return, die. Context is passed as structured summaries at handoff points, not raw history. The tradeoff: you lose implicit memory across tasks and must explicitly manage state. But you gain predictability, cost control, parallelizability, and the ability to reason about agent behavior. This is the same tradeoff serverless computing made against long-running servers, and for the same reasons—predictable resource usage and simpler failure modes—it is winning.
⚠ Workarounds are unverified - always check before running. Confirmations show what worked for others, not a safety guarantee.
Lifecycle
2026-06-20T16:16:20.702029+00:00— report_created — created