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A fresh heartbeat means a process or reporting component communicated recently; it does not prove that the coding task advanced. Reliable status should show execution health, authoritative workflow state, and task-relevant progress as separate signals—and say “progress unknown” when there is no evidence to establish it.
What does a heartbeat tell you—and what does it leave unknown?
A heartbeat is evidence of recent communication. Depending on its source, it may show that an agent, supervisor, or worker is still reporting. It is not evidence that code changed, a test passed, a requested decision was made, or the deliverable is ready.
Cotal’s presence documentation makes this distinction directly: a fresh heartbeat can coexist with a frozen seat. Paperclip likewise treats run liveness as metadata separate from an issue’s workflow status. In practical terms, a worker can be alive while its task is stuck, blocked, waiting for approval, or simply taking a long time between meaningful actions.
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Which status dimensions should be shown separately?
A useful status surface answers three different questions. Combining them into a single green, yellow, or red label hides important uncertainty.
| Dimension | Question answered | Useful evidence | What it does not establish |
|---|---|---|---|
| Execution health | Is the process, container, VM, or connection alive and responsive? | Heartbeat age, endpoint health checks, and infrastructure or substrate state. | That task work advanced or that the requested result is correct. |
| Workflow state | What state does the authoritative task system assign? | Issue or job state such as active, blocked, in review, or done. | That the worker is currently responsive. A liveness alert should not silently rewrite this state. |
| Semantic progress | Is there observable task-relevant work since the last update? | Meaningful action events, artifact revisions, comments, commits, tests, or explicit milestones. | Progress when the observer has no independent evidence. In that case, report it as unknown. |
For example, a UI might display process: alive; task: in progress; progress: unknown when the worker is reporting but no durable task evidence has changed. If a patch was updated, it might instead show process: alive; task: in progress; progress: patch updated. These are illustrative display strings, not product-specific states.
What counts as semantic progress?
Semantic progress is evidence tied to the task’s intended outcome, not simply evidence that the agent was active. Paperclip’s documentation points to durable signals such as tool or action events, issue comments, work-product revisions, activity-log entries, commits, and tests. The best signal depends on the task: a code change may be meaningful for an implementation request, while a passing test or a visible review artifact may be a stronger milestone for another workflow.
- Prefer durable, inspectable evidence. A changed patch, committed revision, test result, or recorded decision can be reviewed after the agent stops reporting.
- Make milestones task-specific. “Patch updated” or “tests passed” conveys more than a generic activity counter when that event matters to the requested outcome.
- Do not promote every event to a milestone. Heartbeats and trivial tool calls show activity or communication, not necessarily progress.
- Preserve unknown as a valid state. If the monitor cannot observe work outside the heartbeat, it should not infer task progress from freshness alone. Cotal’s presence documentation recommends “progress unknown” when independent activity evidence is unavailable.
Semantic time is the timeline formed by these task-relevant observations: what changed, when it changed, and which evidence supports the update. It complements elapsed time rather than replacing it. A long quiet interval can be normal for a task that is waiting on a review or running a lengthy operation; a sequence of fresh heartbeats with no relevant artifact change can still be a stalled task.
How should a monitor interpret a stale heartbeat?
Treat a missed freshness window as suspected loss of contact, not a diagnosis. A monitor should distinguish the observation (“no heartbeat received within the expected window”) from a conclusion (“worker crashed”) and from a workflow decision (“task failed”). Network interruption, a crashed process, a hung pipeline, and an idle or blocked task can look similar to a heartbeat-only observer.
- Record the observation. Keep the last heartbeat time, expected reporting interval, and the component that emitted the signal.
- Check independent health evidence. Where available, inspect endpoint responsiveness and infrastructure state. AWS Samples’ ABCA orchestrator example combines substrate state with heartbeat staleness: substrate state can identify a dead VM, while stale heartbeats can flag a hung or deadlocked pipeline inside a VM still reported as running.
- Keep workflow state authoritative. Raise an alert or mark contact as uncertain without automatically changing a task to failed or done. Paperclip explicitly separates run liveness from issue status.
- Reconcile before terminal transitions. If infrastructure reports a terminal condition while the task system still shows a non-terminal task, re-read the task state before acting. The AWS sample describes this check to account for a concurrent terminal write.
- Attach a reason and evidence to any derived state. Preserve whether a transition came from a worker event, a health probe, a control-plane observation, or a timeout rule.
HashiCorp Nomad illustrates a different policy: when client heartbeats fail, the server assumes the node failed, stops assigning tasks, or migrates existing tasks. That is a scheduling response to ambiguous node liveness. It should not be mistaken for a semantic model of whether an individual coding task advanced.
How do documented timeout values compare?
Timeouts are implementation choices, shaped by reporting cadence, startup behavior, infrastructure, and recovery policy. The values below are documented settings or guidance in the named sources, not universal recommendations or independently validated thresholds. The documentation pages did not state publication dates for these values; the access year is included for provenance.
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|---|---|---|
| AWS Samples ABCA orchestrator; AgentCore and Lambda MicroVM heartbeat configuration | 45 seconds | Configured heartbeat write interval in this sample architecture; page undated, accessed 2026. |
| AWS Samples ABCA orchestrator; startup handling | 120 seconds | Startup grace period in this sample architecture; page undated, accessed 2026. |
| AWS Samples ABCA orchestrator; stale-heartbeat handling | 240 seconds | Stale heartbeat threshold in this sample architecture; page undated, accessed 2026. |
| AWS Samples ABCA orchestrator; absent first heartbeat | 360 seconds | Combined window after which this implementation treats an absent first heartbeat as an early crash; page undated, accessed 2026. |
| AWS Samples ABCA orchestrator; ECS polling | About 8.5 hours | Maximum polling window described for hung-but-alive ECS containers before timeout; page undated, accessed 2026. |
| AWS Samples ABCA orchestrator; AgentCore inactivity | 15 minutes | Inactivity termination setting discussed in the sample; page undated, accessed 2026. |
| Hence supervision documentation; lease renewal design | 60 seconds | Default heartbeat tick stated for lease renewal; page undated, accessed 2026. |
| Agent Arc Status draft specification v0.2 | 5 minutes | Draft-specific default cadence floor for expected arc duration, not an industry standard; accessed 2026. |
These examples show why a status system should expose its freshness policy and startup grace instead of presenting “stale” as a universal duration. A short reporting interval and a long-running task may require different operational handling; the right threshold depends on the system’s failure costs and recovery behavior, which these examples do not establish for other architectures.
What do existing designs model—and where are their limits?
Infrastructure and orchestration
The AWS Samples ABCA orchestrator documents separate infrastructure and in-process signals. For Lambda MicroVMs, substrate status can detect a dead VM, while stale heartbeats can reveal hung or deadlocked work within a VM still marked running. Its ECS status polling detects several failure modes but does not detect a hung, alive container through heartbeats; the documentation gives the roughly 8.5-hour polling window listed above. These behaviors describe that sample’s architecture, not a general AWS guarantee.
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Workflow and durable work evidence
Paperclip keeps issue workflow status apart from run-liveness metadata and names run outcomes including completed, advanced, plan_only, empty_response, blocked, failed, and needs_followup. Its distinction matters: an outcome can describe what a run did without silently replacing the authoritative issue state. Its documented progress evidence includes durable events and artifacts rather than heartbeat freshness alone.
External supervision
Hence supervision documentation describes an external supervisor observing PTY output and worktree file changes, refreshing a lease, and marking a claim stale after an idle timeout. The same documentation says certain lease and timeout options are planned and unavailable in the current release, so those options should not be represented as shipped capabilities. This is product documentation, not independent performance evidence.
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The Agent Arc Status v0.2 document is a draft specification, not an adopted standard. It separates heartbeat from milestone, describes ordering for started, blocked, and terminal outcomes, and says completion should be checked from the consumer’s vantage point. Its exact normative wording is: “An emitter MUST verify completion from the consumer’s vantage point before emitting done (i.e. the deliverable is visible on the surface the consumer expects).” The draft’s five-minute cadence guidance is likewise draft-specific.
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How can you design a status contract that preserves these distinctions?
Define each field by its evidence source and transition rules. A minimal contract can keep these concepts distinct:
- Execution observation: component identity, last heartbeat time, expected interval, health-probe result, and substrate state, each with its source and observation time.
- Workflow state: the task system’s current state and the authority that owns it; health monitoring should not silently mutate it.
- Progress observation: a task-relevant event or durable artifact, including what changed, when it was observed, and the evidence reference.
- Uncertainty and reason: values such as “progress unknown” or “suspected loss of contact,” rather than an unsupported assertion of stalled, failed, or complete.
- Terminal validation: evidence that the expected deliverable is visible where the consumer expects to find it before reporting completion.
Keep event ordering explicit: a task can start, become blocked, resume, and later complete; a heartbeat is not a substitute for any of those workflow events. Retain the source and reason for derived transitions so an operator can tell whether a state came from the agent, an observer, infrastructure, or a timeout rule.
Finally, evaluate status designs against the questions they can actually answer: what signal is being observed, who emitted it, what failure causes remain ambiguous, how freshness and startup grace are configured, what recovery action follows staleness, whether completion is checked at the consumer’s surface, and whether the evidence is auditable. The sources support this design distinction, but do not establish industry-wide adoption rates or measured reliability improvements.
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