← Field Notes
NOV 10 · Clipped · via Martin Fowler Background ExecutionProgressRecovery

Fowler's team maps how agents survive long, failure-prone runs

The infrastructure saves state so a human can judge whether to resume. That is a recovery interaction the platform now enables. Most products leave that judgment invisible, so humans approve nothing and see nothing.

FIG. 1 · Where this sits in Background Execution
    14 NOTES ON BACKGROUND EXECUTION IN 26 WEEKS · SCRUB THE WEEKS, CLICK ONE TO PIN IT
    Published, annotated field notes attached to Background Execution, by week observed · last 26 weeks · n = 14. Rail markers are lifecycle events from the pattern changelog.
    Data table
    Week ofNotes
    AUG 3Claude Code's stacked defenses stop hidden-instruction attacks
    AUG 10A designer ran app maintenance through Claude for weeks from Slack
    AUG 24Karpathy's autoresearch runs experiments start to finish unattended · OpenAI gives ChatGPT tasks that fire on a schedule or a trigger
    SEP 28AI agent runs supercomputer jobs for scientists across four tasks
    OCT 12Vercel lets agents subscribe to live infrastructure events
    OCT 19Anthropic's AI ran a biology lab's image analysis start to finish
    OCT 26ShutdownMod scores whether an AI agent stops when told to · FlashScript turns repeated agent runs into fixed, reusable programs
    NOV 2Uber says almost no riders book trips through AI agents · MIT researchers model when a delegated agent should ask versus act
    NOV 9Fowler's team maps how agents survive long, failure-prone runs · Simon Willison had Claude write code to find a mystery beep · 289 workers sorted tasks they would delegate from tasks they kept

    Machine summary of the source

    Martin Fowler's team published a detailed look at how infrastructure can keep agents running across hours or days without losing work to failures. The core idea: record each step as it completes, so a crashed agent can pick up from the last safe point rather than start over. This changes what a human sees. A long-running task stops feeling like a black box and starts feeling like something with a recoverable history.

    For a designer, the interaction question shifts from 'did it finish?' to 'where did it stop, and can I see why?' The piece argues that agents doing real work need infrastructure that makes failures visible and reversible. A human reviewing a failed run needs to see which step broke, what state the agent held at that moment, and whether resuming is safe. That is a progress and recovery problem as much as an engineering one.

    The piece stays close to the engineering layer, so design implications require reading between the lines. The strongest signal for product teams is this: the longer an agent runs, the more the human relationship shifts from delegation to monitoring, and the more the product must surface what the agent has done so far, not just what it plans to do next.

    The summary above is generated; the note at the top is the editorial judgment. Primary source ↗