docs(agent-note): record implemented goal execution stack

.agents/notes/implemented/feature/2026-07-16-harness-level-loop.i18n.yaml

@@ -0,0 +1,6 @@
+# Bilingual-pair consistency record (docs/i18n/README.md): the git blob hash of each
+# side as of the last confirmed-consistent state. Both languages carry equal authority;
+# after editing either side, bring the other along and re-record with:
+#   pnpm run verify-translation-pairing --write
+2026-07-16-harness-level-loop.md: 76b4efbe42bd0938fcaff14e96ad4e73883e602d
+2026-07-16-harness-level-loop.zh.md: 443501cfaf8a37a210786d4b251e58116c508ba0

.agents/notes/implemented/feature/2026-07-16-harness-level-loop.md

@@ -0,0 +1,126 @@
+# Agent Note: Harness-level goal-based execution
+
+Status: implemented
+
+English | [中文](2026-07-16-harness-level-loop.zh.md)
+
+## Problem
+
+The concrete agent loop owns one turn: it drains admitted input, performs one or more model-and-tool steps, and stops. Substantial objectives often need an outer policy that can begin another turn, retain progress, stop at a budget, and remain intelligible to humans. A timed prompt, a same-session continuation, and a fresh-agent Ralph attempt all repeat work, but they do not share the same state, authority, memory, or lifecycle.
+
+Treating every repeated action as one generic “loop” obscures those differences. Same-session work must persist the human objective in the existing transcript while preserving conversation context. Ralph work must intentionally discard conversation context and use the workspace plus a bounded handoff. Human-facing status must not imply that reopening a session silently authorizes more work. Completion and blocker claims also need an explicit trust boundary rather than being smuggled into a scheduler abstraction.
+
+The repository therefore needs goal-based execution above the turn/step loop, but it does not need a speculative universal loop service that combines persistence, evaluation, budgeting, scheduling, handoff, background tasks, and UI.
+
+## Decision
+
+This proposal is implemented in amended form as two explicit plugin policies over existing seams:
+
+1. **Same-session goals** retain one durable objective in the current session and admit goal-attributed continuation turns only while live activation is armed.
+2. **Fresh-agent Ralph runs** execute a fixed foreground workflow whose rounds each spawn a new structured child with no conversation seed.
+
+There is no `packages/loop/` family, `LoopDriver`, `LoopId`, universal `StopCondition`, or model-facing generic `loop` tool. The two policies share the repository's ordinary agent, session, tools, workflow, subagent, and UI extension seams, but they do not pretend that one lifecycle fits both.
+
+### Vocabulary and policy boundary
+
+The same-session hierarchy is **Goal → Goal Round → Turn → Step**. A goal round is one continuation cycle admitted for the current goal and materialized as one goal-sourced turn. Human or unrelated turns in the same session do not consume the goal-round cap, and a turn may still contain multiple model/tool steps.
+
+The fresh-agent hierarchy is **Ralph Run → Ralph Round → fresh child Turn → Step**. One Ralph round creates one child session. The parent transcript and prior child transcripts are not seed context; the shared workspace and one bounded structured report carry cross-round state.
+
+“Round” is therefore an outer policy iteration, not a synonym for every session turn. The concrete `dsh-agent-loop` remains the turn/step engine. The same-session driver uses public agent and session events; its only core addition is the generic observe-before-cancel `agent/cancel-requested` notification needed by any lifecycle policy that must settle cancellation safely.
+
+Time-based `/loop` or scheduled execution is a third policy and is not implemented by this decision. It belongs with a scheduler rather than either goal family.
+
+### Package topology and owning verbs
+
+| Package | Repository category | Owned structures and verbs |
+|---|---|---|
+| `@deepseek-ai/dsh-goal` | `packages/goal/goal/`, domain service | Owns `GoalId`, compare-and-set `GoalRef`, `GoalSnapshot`, `GoalPhase`, process-local `GoalActivation`, replay folding, and `get`, `create`, `edit`, `pause`, `resume`, `complete`, `block`, `markUsageLimited`, `markBudgetLimited`, `clear`, and `disarm` verbs. |
+| `@deepseek-ai/dsh-tool-goal` | `packages/goal/tool-goal/`, model-facing consumer | Registers exclusive `get_goal`, `create_goal`, and `update_goal`; authenticates live turn provenance and narrows autonomous-round authority to completion or blocking reports. |
+| `@deepseek-ai/dsh-goal-session` | `packages/goal/goal-session/`, continuation policy | Reserves, fences, admits, attributes, settles, cancels, and quiescently drains same-session goal rounds without importing the concrete loop. |
+| `@deepseek-ai/dsh-commands` | `packages/ui/commands/`, UI registry | Owns `CommandDefinition`, discovery, scoped registration, direct dispatch, `CommandResult`, and request cancellation for human-only commands. |
+| `@deepseek-ai/dsh-command-goal` | `packages/goal/command-goal/`, human-command producer | Registers `/goal` status, creation, edit, pause, resume, and clear over the goal domain for TUI and ACP. |
+| `@deepseek-ai/dsh-tool-ralph` | `packages/workflow/tool-ralph/`, fixed workflow consumer | Registers `ralph({ objective, maxRounds? })`, validates the fresh structured provider and bounded `RalphRoundReport`, and returns `complete`, `blocked`, or `budget-limited`. |
+
+The detailed contracts live in the [goal-domain](2026-07-19-persisted-same-session-goal-domain.md), [model goal-tools](2026-07-19-model-facing-goal-tools.md), [goal-round driver](2026-07-19-same-session-goal-round-driver.md), [command registry](2026-07-19-plugin-command-registration.md), [human goal-command](2026-07-19-human-goal-command.md), and [Ralph workflow-tool](2026-07-19-fresh-agent-ralph-workflow-tool.md) Agent Notes.
+
+### Durable goal state and live authority
+
+One session has at most one current goal. Every non-clear mutation appends a full, versioned, model-visible goal snapshot through `Agent.inject()`; clear appends a revisioned tombstone. The session log is the only durable source of truth, so normal persistence, resume, compaction semantics, and `SessionStore.fork()` carry the goal without a second database or an artificial cancellation record.
+
+Durable phases are `active`, `paused`, `blocked`, `usage-limited`, `budget-limited`, and `complete`. Separate activation is `armed` or `disarmed` and is never persisted. Creation and explicit resume arm a goal; stop transitions, session start, fork replay, driver replacement, and driver teardown leave it disarmed.
+
+This separation makes session restoration observable and unsurprising. Reopening a session never starts goal work by itself. A later human prompt such as “continue”, “resume the goal”, or an equivalent request in any language gives the runtime-root model a new turn in which it may read the goal and call `update_goal(..., action: 'resume')`. `/goal resume` is the direct human-command path. The runtime authenticates that the request came from a live direct-human turn; prompt policy lets the model interpret whether the wording semantically authorizes creation or resumption.
+
+Forked sessions inherit the durable goal prefix because that is the natural replay result. The fork starts disarmed, so inheritance does not imply execution authority and no synthetic goal cancellation is inserted into history.
+
+`defaultMaxGoalRounds` is configurable and defaults to `256`. The cap counts only admitted goal rounds. `blockedAfterConsecutiveRounds` is separately configurable in the model-tool policy and defaults to `3`; it is a mechanical lower bound before an autonomous round may report a repeated blocker, not an evaluator of semantic sameness.
+
+### Same-session continuation
+
+The goal-round driver owns at most one pending reservation per exact live agent. It admits a reservation only when the goal is active and armed, the agent is idle, no competing human work exists, pending mutations are durable, the exact goal id/revision/round still matches, and downstream prompt policy accepts it. The prompt-submit fence checks those facts both before and after asynchronous listeners, preventing an edit, pause, human message, or unload race from admitting obsolete work.
+
+Only the durable goal-sourced `user/message` charges a round. Stale reservations become rejected zero-step turns without consuming the cap. A concurrent goal revision wins over settlement from an older round.
+
+Normal turn completion schedules another round only while the goal remains active, armed, and below its cap. Cancellation pauses; rate limiting records `usage-limited`; cap exhaustion records `budget-limited`; other errors, max-token stops, policy rejections, and unknown terminal results block for inspection. The driver never invents an automatic retry after an abnormal outcome. A human can later authorize resume through ordinary language or `/goal resume`.
+
+### Human and model surfaces
+
+The human UX follows the compact current [Codex `/goal` command shape](https://learn.chatgpt.com/docs/developer-commands?surface=cli): `/goal` shows status, `/goal <objective>` creates, and `edit`, `pause`, `resume`, or `clear` perform direct lifecycle actions. Status includes durable phase, admitted/capped rounds, and live armed/disarmed activation. Direct status and command output do not enter model history; accepted domain mutations remain reconstructable because the goal service records them.
+
+The model receives only `get_goal`, `create_goal`, and `update_goal`. It may create a goal when a direct human request clearly asks for substantial multi-round work, and it may infer that intent in any language. It must not turn routine one-turn work into a goal. Direct-human provenance is enforced in code; semantic interpretation remains model judgment. An autonomous goal round may report `complete` or `blocked` for the exact current goal round but cannot edit, pause, resume, or replace the human objective.
+
+TUI and ACP mount the shared command registry and complete goal stack by default and expose `/goal` through one producer. The UI-less agent spine is opt-in so one-shot callers do not silently become multi-round operations. Line-oriented stdio does not consume the command plane; its ordinary human text can still authorize model goal tools when that stack is mounted.
+
+### Fresh-agent Ralph execution
+
+Ralph is a first-class model tool in its own plugin, demonstrating that a sophisticated fixed execution policy can be composed without a new loop core. The plugin owns a fixed workflow script over `ctx.workflows` and `ctx.subagents`; it does not create session-goal state or add a branch to `dsh-agent-loop`.
+
+Each round uses an explicit `WorkflowStartRequest.subagentProvider`, defaulting to `spawn`. The provider must exist, support structured output, and declare that it does not inherit parent context. The child inherits cwd and lineage but receives only the immutable objective, round/cap, workspace-as-authority instruction, and previous normalized report.
+
+A report contains status, summary, evidence, next steps, and blocker text. Status-specific invariants and serialized size are validated inside the fixed script and again at the consumer boundary. `maxRounds` is configurable, defaults to `256`, and is the ceiling for a call override. `maxHandoffChars` defaults to `16384`; oversized reports fail rather than being silently truncated.
+
+The tool is foreground and process-local. The parent tool call waits for the terminal result, propagates cancellation into the worker engine, and awaits `run.dispose()` so child work is quiescent before return. The model sees one call and one bounded terminal result; intermediate child conversations remain outside the parent transcript.
+
+### External design lineage
+
+Codex provides the minimal observable goal UX used here: a persistent chat-attached target with set, view, edit, pause, resume, and clear controls. This implementation adopts that discoverability while using this repository's event-sourced goal record, plugin scopes, and runtime authority checks.
+
+Current [Claude Code goals](https://code.claude.com/docs/en/goal) reinforce the distinction between a goal that starts another turn after the previous turn and a timed `/loop`. Claude Code also uses a separate small-model evaluator after each turn. This implementation adopts the policy distinction but intentionally does not copy that evaluator: evaluator inputs, tool access, deterministic checks, provider choice, isolation, and authority need a separately designed plugin contract rather than an implicit self-certification layer.
+
+External products are comparators, not compatibility targets. The local source studies informed the boundaries, while the shipped interfaces follow this repository's “everything is a plugin”, model-visible-is-logged, explicit default resolution, and quiescent teardown rules.
+
+### Verification
+
+The six owning Agent Notes record unit, integration, process, snapshot, cancellation, replay, and built-runtime coverage. The stack exercises strict goal-record folding, compare-and-set races, session fork inheritance, disarmed restoration, natural-language direct-human authority, configurable caps and blocked thresholds, exact goal-round attribution, command discovery in TUI/ACP, ACP transcript isolation, fixed Ralph provider routing, distinct unseeded children, bounded handoff, terminal outcomes, and cancellation quiescence. Package sources remain under the repository's per-file 100% coverage gate, and the assembled applications are pinned by keyless replay snapshots and built-binary tests.
+
+## Alternatives considered
+
+- **Implement the original universal loop capability seam** — rejected because `Evaluator`, `BudgetPolicy`, `RoundHandoff`, `GoalReflector`, background task ownership, persistence, and scheduling do not form one coherent mandatory abstraction. Building all of them before their first concrete consumers would create broad speculative surface and duplicate existing session, workflow, subagent, and task machinery.
+- **Implement only same-session goals** — rejected because fresh-context iteration is materially different and is a valuable demonstration of the plugin architecture. Ralph belongs as a fixed workflow consumer with explicit context reset.
+- **Put Ralph inside the goal-round driver** — rejected because same-session goals deliberately preserve one conversation while Ralph deliberately removes it. Combining them would make activation, replay, handoff, and UI state ambiguous.
+- **Treat a fork as a fresh Ralph child** — rejected because a fork carries a conversation prefix. Fresh children plus workspace state and one explicit report are easier to bound and replay without a synthetic cancel record.
+- **Copy Claude Code's evaluator into the first goal implementation** — rejected because a transcript-only model evaluator is one useful policy, not a generally trustworthy completion certificate. Deterministic evaluation and isolation must remain possible, so the evaluator is deferred until its authority and provider seam are designed.
+- **Automatically continue after session restore** — rejected because opening a session is observation, not authority to spend resources. Durable state is restored while activation waits for a new human prompt.
+- **Route `/goal` through the model** — rejected because status and explicit lifecycle controls should be deterministic, token-free UI actions; ordinary natural-language prompts remain the semantic model path.
+- **Modify the concrete agent loop with goal or Ralph modes** — rejected because public queue, prompt, session, cancellation, workflow, and subagent seams already support both policies. The generic cancel-requested observation is the only core coordination addition.
+
+## Consequences
+
+- Goal-based execution ships without one overloaded “loop” object: same-session continuation and fresh-agent iteration have explicit, separately testable contracts.
+- Durable goal history is replayable and forkable, while process-local activation prevents accidental work on resume.
+- Humans receive a small Codex-shaped UX; models receive a compact provenance-checked tool surface; deployments can remove either independently.
+- Ralph demonstrates a nontrivial fixed policy entirely as a plugin over existing workflow and subagent primitives.
+- Round limits are generous by default but remain deployment-controlled. They bound iterations, not tokens, price, elapsed time, or external side effects.
+- The original proposal's evaluator, budget, reflector, background-task, CLI, and generic loop-session architecture is intentionally not part of the implemented public surface.
+
+## Known limitations and deferred work
+
+- **Independent evaluation** — same-session completion/blocking and Ralph terminal status are model or worker declarations. A separate evaluator, evaluator-driven feedback round, completion certificate, deterministic checker, adversarial verifier, and criteria/executor/isolation contract remain deferred.
+- **Aggregate budgets** — `maxGoalRounds` and Ralph `maxRounds` are the only aggregate effort limits. Token, currency, elapsed-time, provider-usage, and per-round price admission policies are absent.
+- **No persistent autonomous runner** — same-session goal facts persist, but activation and scheduling are process-local and deliberately wait for human input after restore. Ralph runs are foreground and cannot resume after process loss. Background collection, restart recovery, and unattended resident execution are deferred.
+- **No time scheduler** — interval `/loop`, cron, proactive maintenance, and cloud or desktop scheduling are outside this decision.
+- **No generic loop journal or execution-world rewind** — session replay reconstructs model-visible goal history, not prior files, processes, environment, credentials, or external side effects. Ralph treats the current workspace as authority and carries no cross-run journal.
+- **No goal reflector** — concern events, automatic no-progress heuristics, goal revision by an independent reflector, stuck-pattern detection, and `loop_split` are not implemented. Humans can edit, pause, clear, or resume the goal directly.
+- **Ralph policy remains narrow** — one round creates one fresh child; within-round fan-out, evaluator/worker role separation, dynamic provider/model selection, and structural recursive-Ralph tool denial need separate policy surfaces. Prompt guidance is not enforcement.
+- **Portable UI remains modest** — TUI and ACP render plain-text goal status and generic Ralph cards. There is no continuous status widget, reconnectable command output, modal goal editor, or command plane in stdio/JSON-RPC.

.agents/notes/implemented/feature/2026-07-16-harness-level-loop.zh.md

@@ -0,0 +1,126 @@
+# Agent Note: Harness 层目标式执行
+
+Status: implemented
+
+[English](2026-07-16-harness-level-loop.md) | 中文
+
+## 问题
+
+具体 agent loop 只拥有一个 Turn：它排空已接纳输入，执行一个或多个模型与工具 Step，然后停止。大型目标通常需要一项外层策略来开始另一个 Turn、保留进度、在预算处停止，并让人类能够理解其状态。定时提示词、同会话续行和全新 agent Ralph 尝试都会重复工作，但它们并不共享相同的状态、权限、记忆或生命周期。
+
+若把每种重复动作都称为一个通用“loop”，就会掩盖这些差异。同会话工作必须在现有转录中持久化人类目标，同时保留对话上下文。Ralph 工作必须有意丢弃对话上下文，只使用工作区和一份有界交接。面向人类的状态不能暗示重新打开会话就会静默授权更多工作。完成与阻塞声明也需要显式信任边界，而不能被偷渡进调度器抽象。
+
+因此，本仓库需要位于 Turn/Step loop 之上的目标式执行，但不需要一个把持久化、评估、预算、调度、交接、后台任务和 UI 组合在一起的推测性通用 loop 服务。
+
+## 决策
+
+本提案以修订后的形式实现为构建在现有接缝之上的两项显式插件策略：
+
+1. **同会话目标**在当前会话中保留一个持久目标，并且只在实时激活态已激活时接纳带目标归属的续行 Turn。
+2. **全新 agent Ralph 运行**执行一个固定前台工作流，其中每个 Round 都生成一个不带对话种子的全新结构化子 agent。
+
+系统中没有 `packages/loop/` 包族、`LoopDriver`、`LoopId`、通用 `StopCondition` 或面向模型的通用 `loop` 工具。两项策略共享本仓库普通的 agent、session、tools、workflow、subagent 与 UI 扩展接缝，但不会假装一种生命周期可以同时适配两者。
+
+### 词汇与策略边界
+
+同会话层级是 **Goal → Goal Round → Turn → Step**。一个 Goal Round 是为当前目标接纳的一次续行周期，并实体化为一个带目标来源的 Turn。同一会话中的人类 Turn 或无关 Turn 不会消耗目标回合上限，而一个 Turn 仍可包含多个模型/工具 Step。
+
+全新 agent 层级是 **Ralph Run → Ralph Round → fresh child Turn → Step**。一个 Ralph Round 创建一个子会话。父转录和此前子转录都不是种子上下文；共享工作区与一份有界结构化报告承载跨 Round 状态。
+
+因此，“Round”是外层策略迭代，不是每个会话 Turn 的同义词。具体 `dsh-agent-loop` 仍是 Turn/Step 引擎。同会话驱动器使用公开 agent 与 session 事件；它对核心唯一的新增项是通用的取消前观察通知 `agent/cancel-requested`，任何需要安全收敛取消的生命周期策略都可以使用它。
+
+基于时间的 `/loop` 或定时执行是第三种策略，本决策不实现它。它应归属于调度器，而不是任一目标包族。
+
+### 包拓扑与所属动词
+
+| 包 | 仓库类别 | 所属结构与动词 |
+|---|---|---|
+| `@deepseek-ai/dsh-goal` | `packages/goal/goal/`，领域服务 | 拥有 `GoalId`、比较并交换 `GoalRef`、`GoalSnapshot`、`GoalPhase`、进程本地 `GoalActivation`、重放折叠，以及 `get`、`create`、`edit`、`pause`、`resume`、`complete`、`block`、`markUsageLimited`、`markBudgetLimited`、`clear` 与 `disarm` 动词。 |
+| `@deepseek-ai/dsh-tool-goal` | `packages/goal/tool-goal/`，面向模型消费者 | 注册互斥的 `get_goal`、`create_goal` 与 `update_goal`；认证实时 Turn 来源，并把自治 Round 权限收窄到完成或阻塞报告。 |
+| `@deepseek-ai/dsh-goal-session` | `packages/goal/goal-session/`，续行策略 | 在不导入具体 loop 的情况下，预留、设围栏、接纳、归属、结算、取消并静止排空同会话目标回合。 |
+| `@deepseek-ai/dsh-commands` | `packages/ui/commands/`，UI 注册表 | 拥有面向人类专用命令的 `CommandDefinition`、发现、作用域注册、直接分发、`CommandResult` 与请求取消。 |
+| `@deepseek-ai/dsh-command-goal` | `packages/goal/command-goal/`，人类命令生产方 | 为 TUI 和 ACP 注册构建在目标领域之上的 `/goal` 状态、创建、编辑、暂停、恢复与清除。 |
+| `@deepseek-ai/dsh-tool-ralph` | `packages/workflow/tool-ralph/`，固定工作流消费者 | 注册 `ralph({ objective, maxRounds? })`，验证全新结构化 provider 与有界 `RalphRoundReport`，并返回 `complete`、`blocked` 或 `budget-limited`。 |
+
+详细契约分别由[目标领域](2026-07-19-persisted-same-session-goal-domain.md)、[模型目标工具](2026-07-19-model-facing-goal-tools.md)、[目标回合驱动器](2026-07-19-same-session-goal-round-driver.md)、[命令注册表](2026-07-19-plugin-command-registration.md)、[人类目标命令](2026-07-19-human-goal-command.md)与 [Ralph 工作流工具](2026-07-19-fresh-agent-ralph-workflow-tool.md) Agent Note 拥有。
+
+### 持久目标状态与实时权限
+
+一个会话至多有一个当前目标。每次非清除变更都通过 `Agent.inject()` 追加一份完整、带版本且模型可见的目标快照；清除会追加带修订号的墓碑。会话日志是唯一持久事实来源，因此普通持久化、恢复、压缩语义与 `SessionStore.fork()` 会携带目标，无需第二个数据库或人为取消记录。
+
+持久阶段为 `active`、`paused`、`blocked`、`usage-limited`、`budget-limited` 与 `complete`。独立激活态是 `armed` 或 `disarmed`，且永不持久化。创建与显式恢复会激活目标；停止转换、会话启动、fork 重放、驱动器替换和驱动器拆卸都会让目标保持未激活。
+
+这种分离让会话恢复可观察且符合直觉。重新打开会话绝不会自行开始目标工作。随后的人类提示词，例如“继续”、“恢复目标”或任何语言中的等价请求，会给运行时根 agent 的模型一个新 Turn；模型可在其中读取目标并调用 `update_goal(..., action: 'resume')`。`/goal resume` 是直接人类命令路径。运行时认证请求来自实时直接人类 Turn；提示策略让模型解释措辞在语义上是否授权创建或恢复。
+
+fork 会话会继承持久目标前缀，因为这是自然的重放结果。fork 从未激活状态开始，因此继承不等于执行权限，历史中也不会插入合成目标取消。
+
+`defaultMaxGoalRounds` 可配置且默认为 `256`。该上限只计算已接纳目标回合。`blockedAfterConsecutiveRounds` 在模型工具策略中单独配置且默认为 `3`；它只是在自治 Round 报告重复阻塞前的机械下限，不是对语义相同性的评估器。
+
+### 同会话续行
+
+目标回合驱动器为每个准确实时 agent 至多拥有一个待定预留。只有目标处于活跃且已激活状态、agent 空闲、不存在竞争性人类工作、待定变更已经持久、准确目标 id/修订号/Round 仍匹配，并且下游提示词策略接受时，它才会接纳预留。prompt-submit 围栏在异步监听器前后都检查这些事实，防止编辑、暂停、人类消息或卸载竞争接纳过时工作。
+
+只有持久的目标来源 `user/message` 会计入一个 Round。过时预留会成为未消耗上限的零 Step 拒绝 Turn。并发目标修订会胜过旧 Round 的结算。
+
+普通 Turn 完成后，只有目标仍活跃、已激活且低于上限时才会安排另一个 Round。取消会暂停；速率限制记录 `usage-limited`；上限耗尽记录 `budget-limited`；其他错误、max-token 停止、策略拒绝和未知终止结果会进入阻塞状态以供检查。驱动器绝不会在异常结果后凭空发起自动重试。人类随后可以通过普通语言或 `/goal resume` 授权恢复。
+
+### 人类与模型表面
+
+人类 UX 遵循当前紧凑的 [Codex `/goal` 命令形态](https://learn.chatgpt.com/docs/developer-commands?surface=cli)：`/goal` 显示状态，`/goal <objective>` 创建目标，而 `edit`、`pause`、`resume` 或 `clear` 执行直接生命周期操作。状态包含持久阶段、已接纳/上限 Round 数以及实时已激活/未激活状态。直接状态与命令输出不会进入模型历史；已接受领域变更仍可重建，因为目标服务会记录它们。
+
+模型只接收 `get_goal`、`create_goal` 和 `update_goal`。当直接人类请求清楚要求大量多 Round 工作时，模型可以创建目标，并且可以从任何语言推断该意图。它不得把日常单 Turn 工作变成目标。直接人类来源由代码强制执行；语义解释仍是模型判断。自治目标 Round 可以为准确当前目标 Round 报告 `complete` 或 `blocked`，但不能编辑、暂停、恢复或替换人类目标。
+
+TUI 与 ACP 默认挂载共享命令注册表和完整目标栈，并通过同一个生产方暴露 `/goal`。无 UI agent spine 要求显式选择加入，以免单次调用方静默变成多 Round 操作。行式 stdio 不消费命令平面；挂载目标栈后，它的普通人类文本仍可授权模型目标工具。
+
+### 全新 agent Ralph 执行
+
+Ralph 是位于自有插件中的一等模型工具，展示了复杂固定执行策略可以在没有新 loop 核心的情况下组合完成。该插件拥有构建在 `ctx.workflows` 与 `ctx.subagents` 之上的固定工作流脚本；它不会创建会话目标状态，也不会为 `dsh-agent-loop` 增加分支。
+
+每个 Round 都使用显式 `WorkflowStartRequest.subagentProvider`，默认为 `spawn`。该 provider 必须存在、支持结构化输出，并声明不继承父上下文。子 agent 继承 cwd 与谱系，但只接收不可变目标、当前 Round/上限、以工作区为权威的指令和上一份规范化报告。
+
+报告包含状态、摘要、证据、下一步与阻塞文本。固定脚本内部和消费者边界都会验证状态专用不变量与序列化大小。`maxRounds` 可配置，默认为 `256`，并作为调用覆盖值的上限。`maxHandoffChars` 默认为 `16384`；过大报告会失败，而不会被静默截断。
+
+该工具位于前台且只存在于进程内。父工具调用等待终止结果，把取消传播到工作线程引擎，并等待 `run.dispose()`，因此返回前子工作已达到静止。模型只看到一次调用和一份有界终止结果；中间子 agent 对话不会进入父转录。
+
+### 外部设计谱系
+
+Codex 提供了这里采用的最小可观察目标 UX：一个附着于聊天的持久目标，以及设置、查看、编辑、暂停、恢复与清除控制。本实现采用这种可发现性，但使用本仓库的事件溯源目标记录、插件作用域与运行时权限检查。
+
+当前 [Claude Code goals](https://code.claude.com/docs/en/goal) 进一步验证了“前一 Turn 后启动另一 Turn 的目标”和定时 `/loop` 之间的区别。Claude Code 还会在每个 Turn 后使用独立小模型评估器。本实现采用策略区分，但有意不复制该评估器：评估器输入、工具访问、确定性检查、provider 选择、隔离与权限需要单独设计的插件契约，而不是隐式自我认证层。
+
+外部产品只是比较对象，不是兼容目标。本地源码研究帮助确定边界，而交付接口遵循本仓库“一切皆插件”、模型可见即可记录、显式解析默认值与静止拆卸规则。
+
+### 验证
+
+六份所属 Agent Note 记录了单元、集成、进程、快照、取消、重放与构建后运行时覆盖。该栈验证严格目标记录折叠、比较并交换竞争、会话 fork 继承、恢复后未激活、自然语言直接人类权限、可配置上限与阻塞阈值、准确目标回合归属、TUI/ACP 命令发现、ACP 转录隔离、固定 Ralph provider 路由、互不相同且无种子的子 agent、有界交接、终止结果与取消静止性。包源码继续受仓库逐文件 100% 覆盖率门禁约束，组装后应用由无密钥重放快照与构建后二进制测试固定。
+
+## 考虑过的替代方案
+
+- **实现原始通用 loop 能力接缝**——不予采纳，因为 `Evaluator`、`BudgetPolicy`、`RoundHandoff`、`GoalReflector`、后台任务所有权、持久化与调度并不构成一项一致的必选抽象。在出现首个具体消费者前全部构建，会产生宽泛推测性表面，并重复现有 session、workflow、subagent 与 task 机制。
+- **只实现同会话目标**——不予采纳，因为全新上下文迭代在实质上不同，也是插件架构的重要示范。Ralph 应作为带显式上下文重置的固定工作流消费者。
+- **把 Ralph 放进目标回合驱动器**——不予采纳，因为同会话目标有意保留一段对话，而 Ralph 有意移除对话。合并两者会让激活、重放、交接与 UI 状态含糊不清。
+- **把 fork 当成全新 Ralph 子 agent**——不予采纳，因为 fork 会携带对话前缀。全新子 agent 加工作区状态与一份显式报告更容易限制和重放，并且无需合成取消记录。
+- **把 Claude Code 评估器复制进首个目标实现**——不予采纳，因为只读取转录的模型评估器是一项有用策略，但不是普遍可信的完成证书。系统必须仍能支持确定性评估与隔离，因此评估器延期到其权限与 provider 接缝完成设计之后。
+- **会话恢复后自动续行**——不予采纳，因为打开会话是观察行为，不是花费资源的权限。系统恢复持久状态，而激活态等待新的人类提示词。
+- **通过模型路由 `/goal`**——不予采纳，因为状态与显式生命周期控制应是确定、零 token 的 UI 操作；普通自然语言提示词仍是语义模型路径。
+- **为具体 agent loop 增加目标或 Ralph 模式**——不予采纳，因为公开队列、提示词、会话、取消、工作流与 subagent 接缝已经支持两项策略。通用 cancel-requested 观察是唯一核心协调新增项。
+
+## 后果
+
+- 目标式执行在没有单个过载“loop”对象的情况下交付：同会话续行与全新 agent 迭代拥有显式、可独立测试的契约。
+- 持久目标历史可以重放和 fork，而进程本地激活态会防止恢复时意外开始工作。
+- 人类获得小型 Codex 形态 UX；模型获得紧凑、带来源检查的工具表面；部署可以独立移除任一能力。
+- Ralph 展示了非平凡固定策略可以完全作为现有 workflow 与 subagent 原语之上的插件实现。
+- Round 上限默认宽裕，但仍由部署控制。它限制迭代次数，不限制 token、价格、耗时或外部副作用。
+- 原始提案中的评估器、预算、反思器、后台任务、CLI 与通用 loop-session 架构有意不进入已实现公开表面。
+
+## 已知限制与延期工作
+
+- **独立评估**——同会话完成/阻塞和 Ralph 终止状态都是模型或工作者声明。独立评估器、评估器驱动反馈 Round、完成证书、确定性检查器、对抗式 verifier 与 criteria/executor/isolation 契约均予以延期。
+- **聚合预算**——`maxGoalRounds` 与 Ralph `maxRounds` 是唯一聚合工作量限制。token、货币、耗时、provider 用量与逐 Round 价格准入策略均不存在。
+- **没有持久自治运行器**——同会话目标事实会持久化，但激活与调度只存在于进程内，并且有意在恢复后等待人类输入。Ralph 位于前台，进程丢失后无法恢复。后台收集、重启恢复与无人值守常驻执行均予以延期。
+- **没有时间调度器**——间隔 `/loop`、cron、主动维护以及云端或桌面调度不在本决策范围内。
+- **没有通用 loop 日志或执行世界回退**——会话重放会重建模型可见目标历史，而不会恢复此前文件、进程、环境、凭据或外部副作用。Ralph 把当前工作区作为权威，并且没有跨运行日志。
+- **没有目标反思器**——concern 事件、自动无进展启发式、由独立反思器执行的目标修订、卡住模式检测与 `loop_split` 均未实现。人类可以直接编辑、暂停、清除或恢复目标。
+- **Ralph 策略仍然狭窄**——一个 Round 创建一个全新子 agent；Round 内扇出、评估器/工作者角色分离、动态 provider/模型选择与结构化递归 Ralph 工具禁止都需要独立策略表面。提示词指导不是强制执行。
+- **可移植 UI 仍较朴素**——TUI 与 ACP 渲染纯文本目标状态和通用 Ralph 卡片。系统没有持续状态组件、可重连命令输出、模态目标编辑器，也没有 stdio/JSON-RPC 命令平面。