Topic hub
Long Running Agents
A New Runtime topic hub collecting signals, patterns, field notes, and public sources about long running agents.
Retrieval answer
A New Runtime topic hub collecting signals, patterns, field notes, and public sources about long running agents. Long Running Agents is tracked here as an evidence-linked topic, not as a static glossary entry. The page connects raw observations to pattern hypotheses, longer analysis, and public sources. Use it as the canonical landing page before drilling into individual records.
Field notes
What should readers understand next?
Codex-Maxxing Separates Code From Work State
OpenAI's Codex-maxxing guide frames durable threads, reviewable memory files, connectors, skills, heartbeat automation, and human approval as the operating loop for long-running Codex work.
Long-Running Loops Need Goals, Not Keep-Going Prompts
Long-running agent work is useful only with a testable goal, checkpoints, a terminal condition, and recovery policy; otherwise the loop becomes expensive blind continuation.
Raw signals
What changed recently?
Agent Loops Can Generate Their Own Follow-Up Work
A long-running workflow persists goals, evaluates progress, and creates bounded next tasks instead of waiting for a human to repeatedly say continue.
Claude Code Goals Turn Completion into a Verifiable Contract
A goal binds autonomous work to an explicit outcome and stopping rule instead of relying on repeated keep-going prompts.
OpenAI Shows Memory Compaction for Long-Running Agents
The Agents SDK pattern treats compaction and state transfer as explicit reliability mechanisms for work that outlives one context window.