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A coding agent can be alive without making progress, and a quiet agent is not necessarily dead. A heartbeat shows that a process or reporting component communicated recently; it does not prove that useful work advanced. Reliable status displays therefore keep execution health, workflow state, and task progress as separate signals—and show progress as unknown when there is no evidence to assess it.
Why isn’t a heartbeat proof of progress?
A heartbeat answers a narrow question: did a process, agent, or reporting component communicate within the expected window? It can tell an orchestrator that a worker is still reachable, but it says nothing by itself about whether the worker is reading, coding, waiting, stuck, or producing a result.
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Cotal’s presence documentation explicitly warns that a fresh heartbeat can coexist with a frozen seat. Paperclip likewise keeps run liveness separate from issue workflow status. In both cases, liveness is useful metadata, not a proxy for task advancement.
The reverse inference is also unsafe. A missed heartbeat means the observer has not received an expected signal; it does not identify why. HashiCorp’s Nomad documentation states that a heartbeat failure cannot distinguish a network failure from an agent crash. Depending on the system, a stale signal could also reflect a reporting failure, a hang, an unusually quiet task, or a worker waiting for an approval.
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What should a coding-agent status represent?
Keep at least three dimensions independent. They answer different questions and may legitimately disagree:
| Dimension | Question answered | Evidence to display |
|---|---|---|
| Execution health | Is the process or its infrastructure alive and responsive? | Heartbeat age, endpoint health, container or VM state, and the source and time of the observation. |
| Workflow state | What state does the authoritative task workflow record? | States such as active, blocked, in review, or done, taken from the workflow system rather than inferred from process health. |
| Semantic progress | Has task-relevant work observably advanced? | A meaningful milestone or durable artifact, such as a patch revision, comment, commit, test result, or explicit task event. |
Paperclip’s documentation makes the workflow distinction explicit: run liveness does not replace issue status. It also lists run outcomes including completed, advanced, plan_only, empty_response, blocked, failed, and needs_followup. These describe outcomes, not a universal status vocabulary; a system should define what its own states mean and which component owns them.
A status surface can show the dimensions together without collapsing them: process: alive; task: in progress; progress: unknown, or process: alive; task: in progress; progress: patch updated. The first is more honest than displaying “working” merely because a heartbeat arrived.
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What counts as meaningful progress?
Progress needs task-level evidence. Useful observations are tied to the requested work and can be inspected later: a changed work product, a relevant action event, a comment, a commit, a test result, or a milestone emitted when a defined part of the task is complete. Paperclip’s documentation names tool and action events, issue comments, work-product revisions, activity log entries, commits, and tests as possible evidence.
Do not promote every heartbeat or trivial tool call into a progress milestone. Doing so makes activity look like advancement and obscures the difference between a busy worker and a useful one. Define milestones around observable task outcomes, and retain the underlying evidence so a status consumer can understand why the system reported progress.
When no independent observation is available, report progress unknown. Cotal’s presence documentation uses that distinction when an observer lacks outside activity evidence. Unknown is a valid state: it says what the monitor can establish without inventing certainty.
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How should infrastructure signals and timeouts work?
Infrastructure state and in-process health are complementary, not interchangeable. An AWS Samples ABCA orchestrator example combines substrate status with heartbeat freshness: substrate state can reveal a dead VM, while a stale heartbeat can reveal a hung or deadlocked pipeline inside a VM that is still reported as running. A single signal would miss one of those cases.
Thresholds should be chosen for the architecture and workload rather than copied as universal defaults. The following are documented implementation settings, not general recommendations; the relevant pages are undated and were accessed in 2026.
| Documented setting | Scope and meaning |
|---|---|
| 45 seconds | AWS Samples ABCA orchestrator: configured heartbeat write interval for AgentCore and Lambda MicroVMs. |
| 120 seconds | AWS Samples ABCA orchestrator: startup grace period. |
| 240 seconds | AWS Samples ABCA orchestrator: stale-heartbeat threshold. |
| 360 seconds | AWS Samples ABCA orchestrator: combined window after which an absent first heartbeat is treated as an early crash. |
| About 8.5 hours | AWS Samples ABCA orchestrator: maximum polling window described for hung-but-alive ECS containers before timeout. |
| 15 minutes | AWS Samples ABCA orchestrator: AgentCore inactivity termination setting discussed in the example. |
| 60 seconds | Hence supervision documentation: default heartbeat tick stated for lease renewal in its documented design. |
| 5 minutes | Agent Arc Status draft specification v0.2: default cadence floor for an expected arc duration. This is draft guidance, not an industry standard. |
These figures describe different mechanisms and environments; they are not directly interchangeable timeout recommendations. For example, an expected heartbeat interval, a startup grace period, and a task-level completion deadline solve different problems. Select values based on observed reporting behavior, startup time, task type, and the cost of a false alarm, and expose the chosen threshold in operational documentation.
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What should happen when a heartbeat goes stale?
Treat a missing signal as suspected loss of contact, not a diagnosis or proof that the task failed. Preserve the workflow’s authoritative state while checking independent evidence. A monitor may raise an alert, mark the observation stale, or make a worker eligible for recovery without silently rewriting a task as failed or done.
- Record the observation. Store when the signal was expected, when it was last received, which component supplied it, and which threshold was applied.
- Check independent health evidence. Where available, inspect endpoint health and the container, VM, or other substrate state to distinguish infrastructure failure from an unresponsive process.
- Check task evidence separately. Look for recent task-relevant events or durable work artifacts. If neither progress nor failure can be established, leave semantic progress unknown.
- Apply recovery policy without inventing a cause. Raise an anomaly or make the run retryable according to system policy; keep the reason and evidence attached to the transition.
- Re-read state before a conflicting terminal transition. The AWS Samples ABCA example re-reads task state when terminal substrate status conflicts with a non-terminal task, allowing for a concurrent terminal write.
Nomad illustrates a different policy at the scheduler level: when client heartbeats fail, the server assumes the node failed, stops assigning tasks, or migrates existing tasks. That is node-scheduling behavior; it does not establish semantic progress for an individual coding task.
How should a system verify completion?
Completion is a claim about the result, not merely the worker’s final message. Define the consumer-visible surface that counts as delivery, then verify the result there before setting the task to done. The Agent Arc Status draft specification v0.2 puts this requirement in protocol terms: “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).” It is a draft specification, not a published industry standard.
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For a coding task, the expected surface might be a visible patch, an updated work product, or a result available to the requesting workflow. The precise check depends on the system. Record what was checked and the evidence used so later consumers can distinguish verified delivery from an agent’s assertion that it finished.
How can teams make status auditable?
A useful status model preserves both the current interpretation and the observations that produced it. For each transition, retain the source, timestamp, reason, and relevant evidence. This makes it possible to tell a genuinely stalled run from a stale observer, and to revise a mistaken interpretation without erasing what the system previously saw.
- Label every signal by what it measures: process, infrastructure, workflow, or task progress.
- Keep last-seen time and the applicable freshness window visible to operators.
- Represent unknown separately from no progress, blocked, failed, and done.
- Use milestone definitions tied to durable, task-relevant outcomes rather than raw activity volume.
- Make stale observations trigger diagnosis or recovery policy, not an unsupported task verdict.
- Verify the deliverable at the consumer-facing surface before recording completion.
Hence’s 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 page also says certain lease and timeout options are planned and unavailable in the current release, so those features should not be assumed to be shipped. Its documentation describes a product design, not independent performance evidence.
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