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ZiyaZhang 666ef95f81 fix(tool-goal): let the model deliver a wrap-up message after a goal-round complete/blocked

A goal round reporting complete or blocked used to conclude the turn at
the tool result, so the model never spoke after the call and sessions
ended on a bare update_goal card. The terminal update now defers one
plugin-sourced <goal_complete>/<goal_blocked> instruction onto its
result asking for a grounded closing message without further tool
calls; the turn then ends through the ordinary no-tool-calls stop.
Direct-human mutations stay uninstructed. Wording chosen by A/B
sampling on deepseek-v4-pro; one extra request per goal lifecycle.

New keyless ACP snapshot goal-wrapup drives the shipped app through
create -> round one -> autonomous complete and pins the injection, the
same-turn closing message, and the completed turn end.

2026-08-02 05:58:45 -07:00

4.1 KiB

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Agent Note: Goal-round wrap-up message

Status: implemented

English | 中文

Problem

An autonomous goal round that reported update_goal complete or blocked concluded the physical turn at the tool result, so the model never spoke after the call. Sessions ended on a bare update_goal card, and internal testers read that as the agent stopping mid-sentence: the model's pre-call text routinely announces a report ("goal achieved, marking complete:") that never arrives, because the standard tool-use expectation is one more assistant message after a tool result and neither the goal-round prompt nor the tool description said the call was terminal. The hard stop came from the goal-tool decision, whose turn-stop clause this note supersedes.

Decision

A goal-round complete or blocked success no longer calls concludeTurn(). Instead the tool defers one wrap-up context onto its own result: a { kind: 'plugin', plugin: 'tool-goal' }-sourced user message carrying a <goal_complete>/<goal_blocked> instruction to write a grounded closing message to the user and call no more tools. The turn then ends through the agent loop's ordinary no-tool-calls stop, so no new loop primitive exists and steering semantics are untouched. Direct-human mutations remain uninstructed exactly as before. The cost is one additional model request per goal lifecycle, not per round.

The instruction wording was selected by A/B sampling on deepseek-v4-pro with a reconstructed goal-round transcript: a structured instruction (outcome, verification, artifacts, next steps) consistently beat a minimal "summarize" one on completeness; adding a session-grounding clause shifted unsupported detail from asserted fact to hedged suggestion; and the no-instruction control produced high-variance closings, including confidently fabricated file-level detail.

Scripting the keyless proof required one snapshot-harness addition: dsh-llm-replay resolves {{fromRequest:<regex>}} placeholders in scripted entries against the live request, because a static sidecar cannot know the randomly minted goal id the model must echo into update_goal.

Verification

tool-goal package tests pin the injected context (source, tag, objective, no-more-tools clause) and the absent concludesTurn for both terminal actions, plus the uninstructed direct-human pause and complete paths, at 100% file coverage. llm-replay unit tests pin the placeholder contract: last-match-wins capture, whole-match fallback, and loud failures for unmatched, invalid, and unterminated patterns. The new keyless ACP snapshot goal-wrapup drives the shipped application through create → round one → autonomous complete and asserts the plugin-sourced wrap-up injection, the same-turn closing assistant message, and the completed turn end in both the durable session log and the ACP stdout stream.

Alternatives considered

Surface the completion text on the update_goal UI card — rejected: complete carries no free text today, and adding a summary argument would route a user-facing report through tool arguments while still cutting off the model's natural post-result message.
Keep concludeTurn() and add a "one more text-only step" loop primitive — rejected: new agent-loop machinery for behavior the ordinary stop already provides once nothing concludes the turn.
Instruct inside the tool result content — rejected: the goal tools' canonical output is compact JSON consumed programmatically; a prose instruction block inside it would mix the model-facing contract with the tool's replayable value.

Consequences

Every autonomous goal ends with a user-facing closing message instead of a bare tool card, at the cost of one model request per goal lifecycle. concludeTurn() keeps its loop semantics but loses its only first-party caller outside subagent structured output. Snapshot scenarios can now script values that only exist at run time via {{fromRequest:...}}, which unblocks keyless coverage of any echo-an-id tool flow, goal or otherwise.

4.1 KiB Raw Blame History