mcp: session-notes — petal-dispatch — live experiment session, 2026-06-03
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created: '2026-06-04'
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path: Sources/Dev
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project: execution-journal
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tags:
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- petal-dispatch
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- harness
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- lovebug
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- claude-code
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- experiments
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- execution-journal
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- sse
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type: session-notes
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---
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> **Window:** 2026-06-03 22:14Z – 23:07Z (~53 min)
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> **Host:** herbydev
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> **Dispatch session UUID:** dba5750f-e07f-4dba-8ce2-8f61b80842eb
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> **Active chain:** 134c053b-2774-4255-8750-ab91ec91b675.jsonl
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> **Raw SSE capture:** /tmp/sse_events.jsonl (host-local — not persisted)
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**Workers in window (4):**
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| session_id | uuid | title | status |
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|---|---|---|---|
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| task_cffef42d7c9f | bbe286d0-f34d-4794-b96c-d08c3220903a | harness terminal-event probe | crashed (SIGTERM, exit_code 143) |
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| task_81c483b3703c | 6fca83fe-2dce-4c9f-add5-5067a1ab8216 | three-turn streaming demo | idle (3 turns) |
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| task_aa44afb48ca6 | 55023fdb-e7db-493f-90a4-1e6e37c3408c | three-turn sequential demo | idle (3 turns, gated on read_transcript) |
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| task_4bd577a6d91e | 96538513-8d1a-4108-a89f-dd9bec068961 | dependent-chain demo | idle (3 dependent turns: noun → haiku → Latin) |
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---
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# petal-dispatch — live experiment session, 2026-06-03
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Travis used the petal-dispatch web UI (the v0.5 prototype) to drive a series of experiments against the Dispatch claude. The session was deliberately exploratory: validate the harness emits the event types the design assumes, then poke at multi-turn worker semantics and at how Dispatch-claude streaming actually feels through the UI. A second session (Lovebug-side) was running in parallel and supplying critical feedback on findings, which Travis forwarded into this Dispatch chat verbatim.
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## 1. Experiments run
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### 1.1 Harness terminal-event probe ("does idle ≠ exit?")
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Travis pasted the spec from his other session:
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> In the live PoC, spawn a worker, let it finish, and capture the raw event stream the harness receives. Specifically: does an IDLE (or equivalent) event fire when the worker exits/completes, distinct from any event tied to the worker choosing to emit a message? Dump the raw stream-json around a worker's completion — show the event type, whether it carries the worker's session_id, and the timestamp relative to the worker process actually ending.
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Dispatch's plan, executed in-order:
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1. Read `src/petal_dispatch/tasks.py` and `harness_routes.py` to find which call sites emit which event kinds.
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2. Confirm `/events/stream` is reachable (`curl` against `http://127.0.0.1:8001/events/stream`).
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3. Drop a python SSE-capture script at `/tmp/sse_capture.py` that records each event with wall-clock + monotonic timestamps.
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4. Spawn worker `task_cffef42d7c9f` ("harness terminal-event probe") with prompt `Reply with exactly: DONE-CAPTURE. Nothing else.`.
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5. Let it complete its single turn; observe spawn/user_message/message/idle on the SSE stream.
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6. SIGTERM the worker subprocess to force the terminal-event path; capture the resulting `task.crashed`.
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7. Diff wall-clock kill time vs. crash-event timestamp.
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This was the most data-dense experiment of the session. Output captured at `/tmp/sse_events.jsonl` (5 events).
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### 1.2 Three-turn streaming demo (fire-and-forget queueing)
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Worker `task_81c483b3703c` ("three-turn streaming demo"). Travis prompted:
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> task a worker with these questions, in this order: turn 1 pick your favorite color, turn 2 guess at what my favorite color is, turn 3 give me 5 lines why in programming languages python is better than c.
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Dispatch called `start_task` with turn 1 and then immediately `send_message` for turns 2 and 3 with no gating — three stdin writes back-to-back before the worker had answered any of them. Worker processed them sequentially from its stdin buffer (the pipe queued at the OS layer). Replies: Blue / Green / 5-line Python advocacy.
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### 1.3 Three-turn sequential demo (read-as-gate)
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Worker `task_aa44afb48ca6` ("three-turn sequential demo"). Travis:
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> ok try this execute the turns 2 and 3 only after getting the response from 1 and 2 respectively.
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Dispatch used the `read_transcript format=auto` MCP tool as a serializer — it blocks on the worker's `_turn_done` event before returning. Pattern: `send_message → read_transcript → send_message → read_transcript`. Same final transcript content (Blue / Green / 5-line Python), but on the harness side the three `task.idle` events landed spread out in time instead of bunched at the end.
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### 1.4 Dependent-chain demo (true cross-turn dependency)
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Worker `task_4bd577a6d91e` ("dependent-chain demo"). Goal: prove the orchestrator-side dependency loop works, where each prompt is *built from* the previous reply, not just sent after it. Dispatch picked the example:
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> pick a noun → write a haiku about it → translate its last line to Latin
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Trace:
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- Turn 1 prompt: pick any single noun. Reply: `Lantern`.
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- Turn 2 prompt (built from turn-1 reply): write a 3-line haiku about "Lantern". Reply:
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> Paper skin glowing
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> A small flame remembers night
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> Wind learns how to breathe
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- Turn 3 prompt (built from turn-2 reply): translate the last line — "Wind learns how to breathe" — into Latin. Reply: `Ventus spirare discit`.
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Dispatch noted explicitly: the worker had no way to interpret "the last line of that haiku" — the orchestrator did that extraction and re-quoted it into the next prompt.
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### 1.5 Streaming-UX probes (interspersed conversation, no worker)
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Several turns were not experiments per se but back-and-forth where Travis probed why Dispatch's own replies sometimes felt instant and sometimes delayed by ~15s. These ended up surfacing the streaming-vs-agentic-loop distinction (see §3).
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## 2. Findings (verbatim where possible)
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### 2.1 Five-event signature around worker lifecycle
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From the terminal-event probe, the harness emitted these in this order:
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| seq | mono ts | kind | payload (trimmed) |
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|---|---|---|---|
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| 21 | 518824.2655 | `task.user_message` | `{turn_index:1, message_preview:"Reply with…"}` |
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| 22 | 518824.2673 | `task.spawned` | `{cwd, uuid, title}` |
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| 23 | 518826.7635 | `task.message` | `{turn_index:1, preview:"DONE-CAPTURE", full_message_path}` |
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| 24 | 518826.8250 | `task.idle` | `{last_turn_index:1}` |
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| 25 | 518853.4358 | `task.crashed` | `{exit_code:143, last_error:"DONE-CAPTURE", duration_seconds:29.356}` |
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Every event carries `source: "task_cffef42d7c9f"` (the worker's session_id) and a monotonic `seq`.
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### 2.2 idle ≠ exit (the headline finding)
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Dispatch's verbatim conclusion:
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> Yes — two separate events back-to-back:
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> - `task.message` (seq 23, +0ms baseline) — fires when the worker emits an assistant turn (stream-json `assistant` line).
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> - `task.idle` (seq 24, +61.5ms after the message) — fires on the stream-json `result` line, i.e. turn boundary, while the subprocess is still alive.
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>
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> They're emitted from different branches in `tasks.py:278-298`. `task.idle` does not carry the message body, only `last_turn_index`. Both carry session_id via the harness `source` field.
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And distinguishing two senses of "exits/completes":
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> 1. **Turn complete, process still alive** → `task.idle`. This is the steady state — a v0.5 worker stays running between turns because `--print --input-format stream-json` keeps stdin open. You'll see N `task.idle` events over a worker's life.
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> 2. **Process actually exits** → `task.done` (rc 0) or `task.crashed` (anything else). Fires from `_emit_terminal_event` in the stdout-reader's `finally` block, after the OS reaps the process. Exactly one per worker lifetime.
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### 2.3 Three latent bugs flagged in passing
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Dispatch's own words (these are the findings Travis explicitly wanted captured for the other session):
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**Spawn / user_message ordering race:**
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> `task.spawned` arrived *after* `task.user_message` (1.8ms gap). Both are scheduled via `asyncio.create_task` without ordering guarantees (`tasks.py:_fire`), and `start_task` calls `start_fresh()` (spawn) then `send()` (user_message) on the same tick. Consumers can't rely on spawn being seq-earlier than the first user_message. Probably fine, but if anything downstream assumes "spawn is always first," it's a latent bug.
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**`last_error` misleading on SIGTERM:**
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> `task.crashed.last_error` was populated with `"DONE-CAPTURE"` — the assistant's own reply, because `_last_error_from_tail` picks the last text block when no `result.error` is present. On a clean SIGTERM that's misleading; the field name implies a fault.
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**`task.done` is unreachable in practice:**
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> There is no `task.done` path covered in this capture (would require exit code 0). The v0.5 worker has no natural exit, so `task.done` only fires via the unrouted `manager.kill()` followed by a clean wait — currently unreachable from outside the process.
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### 2.4 Implication for constitution principle #5
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Travis forwarded the other session's framing — that principle #5 (the completion signal must derive from process state, not tool choice) is violated because process-exit is architecturally unreachable in v0.5. Dispatch's response:
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> Honest answer: yes for principle #5 specifically, but it's a different architecture with its own tradeoffs, not a free win. … One-shot fits the "concurrency-only, harness-observed lifecycle" framing better than long-lived. Principle #5 rewrites itself: *completion = subprocess reap, observed by the harness, period.* No conditional sentinels, no /complete endpoint, no flaky-circuit worry.
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Tradeoffs Dispatch laid out for one-shot:
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1. Startup cost per turn (~1–3s claude CLI cold-start, MCP wiring).
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2. "Worker" stops meaning a long-lived process; session_id ↔ conversation UUID on disk, "running" only describes the brief spawn-to-exit window.
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3. No in-memory state between turns (probably fine — `--resume` rehydrates from disk).
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4. Task-complete vs. turn-complete distinction moves up to the orchestrator — every reply is a `task.done`, and whether the *task* (user intent) is done becomes Lovebug's call. Dispatch noted this is "exactly the synthesis-layer hook Lovebug's instance-2 wanted, just expressed structurally instead of via a sentinel."
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### 2.5 Agreed v0.6 shape (Travis confirmed)
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After the back-and-forth, Travis confirmed:
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> <10% follow-up + 30-60s tolerance means one-shot default is the right base — the 2-4s cold start is well inside the budget, and you're not paying it 90% of the time anyway.
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The shape they aligned on, to be implemented when the "real tool" is built:
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- **Default:** spawn-per-turn, `task.done` on real process exit, principle #5 satisfied literally.
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- **Toggle (off by default):** keep worker alive between turns + add a `/complete` (or stdin-EOF) path so `task.done` is still reachable cleanly.
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- **UI:** per-worker status pill that distinguishes spawn/run/idle states visually so users don't read 4s of nothing as "broken."
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### 2.6 `send_message` queueing semantics (incidental finding from §1.2/1.3)
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Dispatch confirmed (with code-level specificity):
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> `send_message` writes to the worker's stdin immediately and returns `{ok:true}`. If the worker is mid-turn, your message sits in the OS pipe buffer and gets picked up after the current `result` line. So yes, queuing happens — but at the pipe level, silently, with no visibility.
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Three options Dispatch sketched for making this explicit:
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1. `send_message` waits for idle before writing stdin (one-liner: `await self._turn_done.wait()` at top of `Worker.send`). Honest, but the MCP call can block 30–60s on a busy worker.
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2. `send_message` refuses if busy, returns `{ok:false, error:"busy"}`. Pushes retry logic onto callers.
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3. Add a `wait_for_idle` tool, keep `send_message` fire-and-forget.
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Dispatch's lean: **option 3** — "Keeps `send_message` honest (it does one thing, fast), and gives callers an explicit 'I'm choosing to wait' verb that maps to the harness `task.idle` event." Composes across workers too.
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## 3. Streaming-UX findings (Dispatch self-observation)
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Travis noticed Dispatch sometimes felt instant and sometimes delayed by 15s. Dispatch's diagnosis, verbatim:
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> Streaming is on the whole time. What you're feeling is **time-to-first-token**, and it has two main sources, neither of which I control:
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>
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> 1. **Extended thinking.** For harder prompts … the model engages thinking blocks before any visible text. Those don't render in the UI — they look like dead air, then text starts streaming at full speed.
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> 2. **Input processing.** Longer prompt = longer prefill before the first output token.
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UX recommendation (Dispatch):
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> A "thinking…" indicator that flips on the moment a request is sent and flips off when the first real token arrives covers all three cases (worker spawn, model thinking, prefill) with one mechanism.
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And the related streaming-vs-agentic-loop clarification:
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> Streaming and the agentic loop are orthogonal: streaming is "within a turn," the loop is "across turns." … What looked like "Claude posting in parts" was actually 6-7 separate turns chained together by tool results.
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## 4. Open questions / next moves
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- **Decision on principle #5 wording.** Two options framed in §2.4. Travis leaned one-shot-default with opt-in long-lived. Not yet written into the constitution / plan document.
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- **`task.done` reachability path.** If long-lived workers stay supported even as a toggle, design the `/complete` endpoint (or stdin-EOF) so the terminal-event path is deliberately reachable, not only via SIGTERM.
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- **`last_error` semantics on SIGTERM.** Distinguish "I was killed cleanly" from "I crashed with an error." Today `_last_error_from_tail` falls back to the last assistant text block, which is misleading. Cheap fix: populate `last_error` only when there's an actual `result.error` or the exit code maps to a fault signal; otherwise leave it null.
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- **Spawn/user_message ordering.** Either guarantee `task.spawned` is seq-earlier than the first `task.user_message`, or document that consumers must not rely on it. Probably the former — small refactor in `tasks.py:_fire` / `start_task`.
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- **Live worker streaming into main chat?** Travis: "I am getting turn three response in the summary, but not separate after it return. no big deal, unless its gated somehow." Dispatch's recommendation: keep the sidebar as the worker's stage, main chat as orchestrator voice — don't pipe worker SSE into the chat pane. Open as a deliberate design call.
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- **More testing with the real tool.** Travis: "maybe more testing is needed? probably with the real tool once we have it." Explicit deferral for the long-lived vs. one-shot UX feel.
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- **Memory save?** Dispatch asked "Worth saving any of this to memory before you flip back to the other session, or hold it loose until you build?" — Travis did not answer that directly in-session.
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## 5. Conversation timeline (UTC)
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| time | who | content |
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| 22:14:16 | user | "hi, are you still there" |
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| 22:14:18 | dispatch | "Yep, still here. What's up?" |
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| 22:15:06 | user | mentions building a new tool, current UI is a prototype |
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| 22:15:28 | user | pastes the harness-event-probe spec from the other session |
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| 22:15:35 | dispatch | states plan: read tasks.py + harness.py, then capture SSE around worker exit |
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| 22:15–22:18 | dispatch | reads code, confirms `/events/stream` reachable, drops `/tmp/sse_capture.py` |
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| 22:19:08 | dispatch | starts capture script in background |
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| 22:19:45 | harness | `task.spawned` + `task.user_message` for `task_cffef42d7c9f` |
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| 22:19:47 | harness | `task.message` "DONE-CAPTURE" + `task.idle` |
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| 22:20:14 | harness | `task.crashed` exit_code=143 after SIGTERM (163ms post-signal) |
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| 22:20:58 | dispatch | full answer landed: 5-event table, idle≠exit explanation, 3 latent bugs flagged |
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| 22:35:50 | user | "great, that was helpful, i have a follow up question…" |
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| 22:37:00 | user | pastes Lovebug-instance-2's principle-#5 / idle-≠-exit critique |
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| 22:37:55 | dispatch | answers: one-shot fixes #5 literally; lays out 4 tradeoffs; lean = one-shot if 2-4s latency OK |
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| 22:42:37 | user | "1-3s is fine. … toggle, off by default … more testing needed" |
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| 22:42:47 | dispatch | confirms: one-shot default + opt-in long-lived; defer until real tool |
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| 22:44:53 | user | "follow-up < 10% … 30-60s tolerance" |
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| 22:45:05 | dispatch | confirms shape: one-shot default + UI status pill |
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| 22:47:25 | user | "30s tolerance ok but I want instant feedback! are you controlling streaming?" |
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| 22:47:46 | dispatch | not gating; TTFT = thinking + prefill; UX fix = "thinking…" indicator on send |
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| 22:49:09 | user | "then how did the one chat … come in parts" |
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| 22:49:24 | dispatch | streaming-vs-agentic-loop distinction; perceived "parts" = 6-7 chained turns |
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| 22:51:14 | user | proposes a three-turn riff (color/guess/python) |
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| 22:51:40 | dispatch | answers all three in one streamed turn (teal / green / 5 lines), notes single-turn-single-stream |
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| 22:52:41 | user | "task a worker with these questions, in this order …" |
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| 22:52:49 | dispatch | spawns `task_81c483b3703c`, sends turns 1+2+3 back-to-back (fire-and-forget) |
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| 22:53:08 | dispatch | reports results; explains stdin pipe queueing |
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| 22:54:36 | user | "execute the turns 2 and 3 only after getting the response from 1 and 2" |
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| 22:54:42 | dispatch | spawns `task_aa44afb48ca6`, gates each send on `read_transcript(auto)` |
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| 22:55:07 | dispatch | reports results; explains temporal-shape difference from previous run |
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| 22:58:12 | user | "do you have to queue the messages? is there a way to send … after getting the response?" |
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| 22:58:48 | dispatch | 3-layer answer; recommends option 3 (`wait_for_idle` tool + fire-and-forget send) |
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| 23:02:19 | user | "does that mean I can give you three tasks … that need answers from the previous before the next?" |
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| 23:02:35 | dispatch | yes — already demonstrated; offers to run a real dependent chain |
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| 23:04:27 | user | "lets try an example of three turns … I cant" (cut off) |
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| 23:04:32 | dispatch | asks for clarification |
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| 23:04:50 | user | "I cant think of a good example, please pick one" |
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| 23:05:03 | dispatch | picks: noun → haiku → Latin translation of last line |
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| 23:05:05 | dispatch | spawns `task_4bd577a6d91e`; chains 3 dependent turns |
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| 23:05:32 | dispatch | reports: Lantern → haiku → "Ventus spirare discit"; reflects on orchestrator's role |
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| 23:06:47 | user | "interesting, I am getting turn three response in the summary, but not separate after it return. no big deal, unless its gated somehow" |
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| 23:07:08 | dispatch | not gated, not streamed live; `read_transcript` returns full reply as one chunk; recommends sidebar as worker stage |
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## 6. Tooling observations on petal-dispatch itself
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- **`task.crashed` after clean SIGTERM is currently the only way to drive the terminal-event path.** `task.done` (rc 0) is structurally unreachable in v0.5 because long-lived workers never naturally exit.
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||||||
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- **Spawn-time ordering is asyncio-task-creation-order, not wall-clock-or seq order.** `task.user_message` landed 1.8ms before `task.spawned` in the probe capture. Both fired from `_fire()` via `asyncio.create_task` without sequencing.
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- **`last_error` on `task.crashed` falls back to the worker's last text block when there's no `result.error`.** Misleading semantics — implies a fault when the cause was just SIGTERM.
|
||||||
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- **Three-event terminal-latency:** SIGTERM → `task.crashed` event landed on the SSE stream ~163ms later. That's the stdout-EOF-propagation + event insert + LISTEN/NOTIFY + SSE fanout path measured end-to-end.
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||||||
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- **`read_transcript format=auto` is a usable serializer.** It blocks on `_turn_done` and returns the rendered transcript including the latest turn. Used implicitly in the sequential and dependent-chain demos to serialize sends — works, but conflates "wait for idle" with "read the transcript," which is why Dispatch suggested splitting them in v0.6.
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||||||
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- **Stream-vs-loop confusion is real for the operator.** Travis read multi-turn agentic output as "Claude streaming in parts." Dispatch diagnosed it correctly as 6–7 chained turns separated by tool executions, but the UI doesn't make that distinction visible.
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|
- **`send_message` returns `{ok:true}` immediately**, regardless of worker busy state. The queueing happens silently in the OS pipe buffer.
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||||||
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- **`/tmp/sse_events.jsonl` is the captured raw stream from the probe** (with wall + monotonic timestamps per event). Useful artifact for any later replay / regression test of the harness event ordering.
|
||||||
|
|
||||||
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## 7. Cross-reference
|
||||||
|
|
||||||
|
- Strategic context for the principle-#5 question lives in trellis thread 586 (`execution-journal-synthesis-layer`).
|
||||||
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- The "Lovebug-instance-2" feedback Travis pasted in §1 is the source for the rewritten-principle-#5 language proposed in §2.4.
|
||||||
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- This session's experiments are the empirical input that should feed back into the v0.6 plan (one-shot default, opt-in long-lived, `wait_for_idle` tool, fixed `last_error` semantics, ordered spawn-vs-user_message).
|
||||||
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Reference in New Issue
Block a user