A task’s current status answers what is true now; it cannot show how the task reached that point or where its time went. In an implementation account for AgentRQ, Mustafa Turan describes keeping the familiar current-status field while adding an append-only history of transitions. That design supports duration reporting without requiring every existing status reader to be rebuilt around an event stream.
Why a current status is not enough
A task marked complete might have moved directly from active work to completion—or spent hours blocked, waited for a person, and then returned to active work. An overwritten status field retains only the latest label, so it cannot answer “How long did it take?” or distinguish agent work from time spent waiting.
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Turan’s design treats the current status and the transition history as complementary records. The current status remains the practical answer for queues, filters, counters, and MCP calls. The history answers how the task changed over time. As he puts it, “The current state is a projection; the transitions are the data.”
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What the history records—and how durations are calculated
Each status transition adds a record containing the state the task left, the state it entered, the time of the change, and the agent or model attribution. Records are appended rather than edited or inserted retroactively. A state’s duration is the time from its transition record to the next one; if it is still the current state, its interval runs up to the present.
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The implementation records a transition in the storage layer’s task-save path, in the same transaction as the status change. Non-status edits do not create transitions. The author’s account says the task is locked during recording so a subsequent change sees the preceding change as its starting state. That is a property of this locking and transaction design—not an automatic guarantee of append-only storage in every system.
Three ways to represent task state
| Approach | What it preserves | Main trade-off |
|---|---|---|
| One overwritten status field | The task’s current state, which is straightforward for existing readers to consume. | Past transitions and evidence for calculating state durations are lost. |
| Current status plus append-only history | Existing present-state reads, along with transitions and timeline-based durations. | Adds history storage and a write for each status change; the history must be kept consistent with the current field. |
| Full event sourcing | The event stream is used to reconstruct current state as well as the task’s past. | Existing readers must be adapted to derive state from events. Turan says this was unnecessary for the duration use case and would require changing many readers, including agent MCP clients. |
AgentRQ uses the middle approach because its existing readers rely on current status. The choice reflects that product’s compatibility needs; it is not a universal prescription. A system already built around event sourcing may have different constraints.
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How human waits fit without changing the task’s real status
Waiting for a person is different from being blocked because the agent cannot proceed. Turan’s design records “needs input” as a derived interval in the history rather than making it a new task status. The task’s actual status can remain unchanged while a question is open, avoiding a status change that could affect agents, queues, filters, or counters.
When at least one real question is unanswered, the timeline can show a human-wait interval. Once the last open question is answered, the history returns to the task’s actual status. The implementation account describes three rules for maintaining that derived state:
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- Count open questions rather than flipping a single flag, so answering one question does not end the wait while another remains open.
- Count actual questions, not pending to-do items.
- Do not reopen the history interval when a question appears on a task that is already complete.
This depends on every question-creation and question-answer path checking the derived state. A missed path can make the timeline inaccurate even if ordinary status changes are captured correctly.
What the dashboard measures
The described task view reports four separate durations. “Start-to-close” begins at the first transition to ongoing, not at task creation, so time queued before work begins is excluded. The timeline and dashboard use the same duration calculation.
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| Measure | What it represents |
|---|---|
| Worked time | Time attributed to active work states. |
| Blocked time | Time in a blocked state where the agent cannot proceed. |
| Needs-input time | Time represented in history as waiting for a human response. |
| Start-to-close time | Elapsed time from the first ongoing transition to closure. |
These are distinct measurements, not parts that must add up to the same total. Each metric’s median is calculated separately, so the medians need not sum to the start-to-close median.
Reading aggregate latency numbers
The described Task Latency panel appears in workspace and account statistics. For a selected range, it reports p50, minimum, and maximum for the four duration measures. For account statistics, histogram bucket counts are merged and p50 is interpolated within the bucket containing the median, then clamped to the exact minimum and maximum. The resulting p50 is an estimate rather than a median obtained by sorting every underlying task duration; it is exact when the relevant bucket contains one task.
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In a 2026 illustrative example, Turan describes 42 tasks closed in a week, with p50 values of 108 minutes start-to-close, 66 minutes worked, 19 minutes blocked, and 7 minutes waiting for a person. These are example dashboard figures, not an independent benchmark or a prediction of typical agent performance. The p50 describes a typical task more usefully than an average when durations have a long tail; the maximum, meanwhile, can help surface outliers.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What this design cannot tell you
- It cannot restore transitions from before history recording began. Existing tasks start their timeline at their next status change. A one-time dashboard backfill can use only whatever history exists.
- “Immutable” does not mean permanent retention. In the described lifecycle, moving a task moves its history, and deleting the task deletes its history.
- State identifiers need stable meanings. The implementation uses a closed state set with an explicit
noneinitial state and is designed to add enum values only at the end. Reordering or renaming stored values would change their meaning. - History adds write and maintenance costs. Each status change brings an additional write, and the derived human-wait state depends on all relevant question paths checking it correctly.
- Aggregate percentiles can be approximate. Merged histogram buckets do not preserve the exact underlying distribution in every case.
These details are from Mustafa Turan’s implementation account for AgentRQ, published on DEV Community on September 30, 2026. They describe that system’s design and behavior, not an independent audit of all agent task trackers.
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