docs: add long-horizon reliability fixes learning + scrub CONCEPTS.md
- New solution doc: logic-errors/long-horizon-reliability-code-review-fixes.md Documents 13 code-review fixes (2 P0, 5 P1, 6 P2) across U1-U7 long-horizon reliability features (disclosure_level default, resume plan_id mismatch, middleware dataclass compat, state offload readback, checkpoint dedup, dynamic phase persistence, debate count restore, loop detection reset, concurrent resume lock, FAILED phase handling, checkpoint cleanup, offload type guard). - CONCEPTS.md: add Expert Orchestration cluster (Disclosure Level, State Offloading, Pipeline Checkpoint, Debate Phase, Resume). Scrub Bitable entries to remove implementation specifics per vocabulary rules (API paths, library calls, SQL syntax, class names, enum values).
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CONCEPTS.md
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CONCEPTS.md
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@ -5,10 +5,27 @@ Shared domain vocabulary for this project — entities, named processes, and sta
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## Bitable
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### Bitable
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A companion service providing multi-dimensional table storage (structured records with typed fields, views, and formula columns) that runs alongside the main AgentKit server. Owns its own PostgreSQL schema and exposes a REST API at `/api/v1/bitable`. Agents interact with it over HTTP using an internal service token; end-users interact via JWT auth.
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A companion service providing multi-dimensional table storage (structured records with typed fields, views, and formula columns) that runs alongside the main AgentKit server. Owns its own database schema, isolated from core tables, and exposes a REST API boundary that all callers — agents and end-users alike — go through. Agents authenticate with an internal service token; end-users authenticate via JWT.
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### Field Ownership
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The model controlling which actor may modify a field's definition or values. Every field has an owner of either `agent` or `user`. Agent-owned fields are written by the BitableTool during data ingestion; user-owned fields are edited by humans in the UI. Upsert operations update only agent-owned fields via `jsonb_set` — user-owned fields are never overwritten by agent writes.
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The model controlling which actor may modify a field's definition or values. Every field has an owner of either the agent or the user. Agent-owned fields are written by the ingestion tooling during data import; user-owned fields are edited by humans in the UI. Upsert operations update only agent-owned fields — user-owned fields are never overwritten by agent writes, so human edits survive re-ingestion.
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### Recalc
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The background process that recomputes formula field values after source data changes. When a record's data columns are written, a recalc task is enqueued; a RecalcWorker claims tasks atomically (FOR UPDATE SKIP LOCKED), evaluates the formula DAG in topological order, and writes results back. The reaper resets stale `calculating` tasks to `pending` for crash recovery.
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The background process that recomputes formula field values after source data changes. When a record's data columns are written, a recalc task is enqueued; a worker claims tasks atomically using row-level locking so concurrent workers partition the queue without coordination, evaluates the formula DAG in topological order, and writes results back. A reaper resets stale in-progress tasks back to pending for crash recovery.
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## Expert Orchestration
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### Disclosure Level
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The mode controlling how much of a skill's prompt enters the agent context at load time. Full mode injects the complete skill prompt; summary mode injects only name and description. The default is full so that skills predating the field keep their original behavior — only skills that explicitly opt into summary mode are degraded.
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### State Offloading
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The pattern of moving a phase's full output to a shared workspace while keeping only a short summary and a reference key in memory. Prevents unbounded memory growth across long pipelines. Consumers that need the full content resolve it through the reference key, not by reading the in-memory summary.
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### Pipeline Checkpoint
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A crash-recovery snapshot of a pipeline's progress, persisted per phase. On resume, completed phases are skipped, failed phases are preserved as failed, and pending phases execute. Checkpoints are cleared after successful completion; failed runs retain them for retry.
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### Debate Phase
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A phase dynamically inserted into a team plan when divergence is detected between expert outputs. Subject to a per-pipeline limit that persists across resume — the counter is rebuilt from the restored plan, not reset to zero.
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### Resume
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The act of rebuilding a crashed pipeline's runtime state from persisted checkpoints. Restores completed and failed phase statuses, rebuilds runtime counters, and re-persists any dynamically inserted phases so the restored plan matches what was executing at crash time.
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@ -0,0 +1,320 @@
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---
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title: "U1-U7 长程可靠性优化代码审查修复"
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date: 2026-06-25
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category: docs/solutions/logic-errors
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module: experts/orchestrator
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problem_type: logic_error
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component: service_object
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symptoms:
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- "All skills silently degraded to summary mode because SkillConfig.disclosure_level defaulted to 0 instead of 1"
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- "Task resume endpoint mismatched task_id and plan_id parameters, causing resume failures on long-horizon tasks"
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- "LoopDetectionMiddleware never triggered loop detection due to dead code (isinstance dict check on a dataclass)"
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- "TokenUsageMiddleware raised AttributeError by accessing token_usage instead of total_tokens on the usage object"
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- "Team synthesis used a truncated 500-char summary instead of the offloaded full content, losing critical context"
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root_cause: logic_error
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resolution_type: code_fix
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severity: high
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tags: [code-review, logic-error, long-horizon-reliability, expert-orchestrator, middleware, checkpoint, react-engine, skill-config]
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---
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# U1-U7 长程可靠性优化代码审查修复
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## Problem
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U1-U7 长程可靠性优化特性分支的代码审查发现 14 个问题（2 P0、5 P1、7 P2），分布在 6 个源文件中，涵盖默认值回归、断点续跑标识符错配、中间件与数据类格式不兼容、状态卸载后内容未回读、检查点去重缺失、动态阶段未持久化、状态泄漏与类型守卫缺失等多类逻辑缺陷。其中 13 个已修复，1 个为误报。
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## Symptoms
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各缺陷在运行时表现为以下可观测症状：
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- **技能降级（P0 #1）**：所有既有技能在加载时只注入名称与描述，完整 prompt 不再进入上下文，技能行为退化为"概要模式"，输出质量明显下降但无报错。
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- **断点续跑失败（P0 #2）**：调用 `POST /tasks/{task_id}/resume` 时返回 404「No checkpoint found」，尽管 checkpoint 实际已写入 Redis；并发 resume 还可能导致同一计划被重复恢复。
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- **中间件静默失效（P1 #3、P1 #4）**：`LoopDetectionMiddleware` 的循环检测从不触发警告（`isinstance(step, dict)` 恒为 False），`TokenUsageMiddleware` 的 `token_usage_total` metadata 始终缺失（属性名错配）。
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- **综合结果残缺（P1 #5）**：Lead Expert 综合阶段产出时只能看到每个阶段前 500 字符的截断摘要，长产出被腰斩，最终综合结果丢失关键细节。
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- **检查点重复累积（P1 #6）**：内存降级模式下，同一阶段被多次 checkpoint 时在 list 中重复出现，`load()` 返回的"最后完成阶段"可能错位。
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- **resume 看不到动态辩论（P1 #7）**：分歧检测插入的 DEBATE 阶段在崩溃后无法被 resume 恢复，因为 `save_plan` 只在流水线启动前调用过一次。
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- **debate 限额失守（P2 #8）**：resume 后 `_debate_count` 归零，`MAX_DEBATES` 上限可被绕过，导致无限辩论。
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- **循环检测状态泄漏（P2 #9）**：同一 `ReActEngine` 实例跨会话复用时，上一会话的 `_loop_window` 残留，新会话首步即可能误判循环。
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- **FAILED 阶段被重跑（P2 #11）**：resume 把 FAILED 阶段当作 PENDING，导致已失败阶段被重新执行，可能再次失败或产生副作用。
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- **检查点永不清理（P2 #13）**：成功完成后 checkpoint 数据仍留在 Redis/内存，7 天 TTL 内持续占用空间。
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- **`_offload_result` 崩溃（P2 #14）**：当阶段产出为 dict 或 None 时，`len(content)` 与切片操作抛 `TypeError`，整条流水线中断。
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## What Didn't Work
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- **误报一项**：审查中标记的一条问题经核实为误报（false positive），未做改动。这提示在批量审查中需对每条 finding 单独验证，避免引入回归。
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- **属性名猜测**：`TokenUsageMiddleware` 最初按"通用命名习惯"假设 `token_usage` 属性存在，但 `ReActResult` 数据类实际使用 `total_tokens`。仅靠阅读中间件自身代码无法发现，必须交叉核对结果数据类的字段定义。
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- **格式假设**：`LoopDetectionMiddleware` 假设 trajectory 步骤是 dict，但 `ReActEngine` 实际存入的是 `ReActStep` dataclass。中间件与引擎分属不同模块，格式契约未显式文档化，导致 `isinstance` 检查静默失效而非报错。
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## Solution
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### P0 #1：SkillConfig.disclosure_level 默认值
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`src/agentkit/skills/base.py` — 将默认值从 0（概要模式）改为 1（全量加载），保持向后兼容。
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```python
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# Before
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disclosure_level: int = 0, # 概要模式 — 所有既有技能被降级
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# After
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disclosure_level: int = 1, # 默认全量加载，向后兼容；0=概要模式需显式指定
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```
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### P0 #2：resume 端点 task_id/plan_id 错配 + 并发防护
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`src/agentkit/server/routes/tasks.py` — 新增 `plan_id` 查询参数，并加 `asyncio.Lock` 防并发 resume（同时关闭 P2 #10）。
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```python
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# Before
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@router.post("/tasks/{task_id}/resume")
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async def resume_task(task_id: str, req: Request):
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...
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plan_dict = await checkpoint.load_plan(task_id) # task_id 当 plan_id 用，永远 404
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# After
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@router.post("/tasks/{task_id}/resume")
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async def resume_task(task_id: str, req: Request, plan_id: str | None = None):
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...
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resolved_plan_id = plan_id or task_id
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# P2 #10: 并发 resume 防护
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lock_attr = f"_resume_lock_{resolved_plan_id}"
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lock = getattr(app_state, lock_attr, None)
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if lock is None:
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lock = asyncio.Lock()
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setattr(app_state, lock_attr, lock)
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if lock.locked():
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raise HTTPException(status_code=409, detail=f"Resume already in progress for plan '{resolved_plan_id}'")
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async with lock:
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plan_dict = await checkpoint.load_plan(resolved_plan_id)
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```
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### P1 #3：LoopDetectionMiddleware 死代码
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`src/agentkit/core/middleware.py` — 同时兼容 dataclass 与 dict。
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```python
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# Before
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for step in recent:
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if isinstance(step, dict): # ReActStep 是 dataclass，恒为 False
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name = step.get("tool_name", "")
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args = step.get("arguments", {})
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# After
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for step in recent:
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if isinstance(step, dict):
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name = step.get("tool_name", "")
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args = step.get("arguments", {})
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else:
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name = getattr(step, "tool_name", "") or ""
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args = getattr(step, "arguments", {}) or {}
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```
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### P1 #4：TokenUsageMiddleware 属性名
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`src/agentkit/core/middleware.py` — 改用 `ReActResult` 实际字段名。
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```python
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# Before
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usage = getattr(result, "token_usage", None) # 不存在，恒为 None
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# After
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usage = getattr(result, "total_tokens", None) # ReActResult.total_tokens
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```
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### P1 #5：综合阶段使用截断摘要
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`src/agentkit/experts/orchestrator.py` `_synthesize_results` — 对 offloaded 结果调用 `_read_dependency_output()` 从 SharedWorkspace 读回完整内容。
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```python
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# Before
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for i, ph in enumerate(completed_phases):
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r = ph.result or {}
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content = r.get("content", str(r)) # content 已被 _offload_result 截断到 500 字符
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# After
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for i, ph in enumerate(completed_phases):
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r = ph.result or {}
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if isinstance(r, dict) and r.get("_offloaded"):
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content = await self._read_dependency_output(ph) # 从 workspace 读完整内容
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else:
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content = r.get("content", str(r)) if isinstance(r, dict) else str(r)
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```
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### P1 #6：检查点内存去重
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`src/agentkit/orchestrator/checkpoint.py` — 内存降级存储由 list 改为 dict keyed by phase_id。
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```python
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# Before
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self._memory: dict[str, list[CheckpointData]] = {}
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...
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self._memory.setdefault(plan_id, []).append(data) # 同一 phase_id 重复累积
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# After
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self._memory: dict[str, dict[str, CheckpointData]] = {}
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...
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self._memory.setdefault(plan_id, {})[phase_id] = data # 同一 phase_id 覆盖
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```
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`list_checkpoints` 的内存降级分支相应改为 `.values()` 迭代。
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### P1 #7：动态 DEBATE 阶段未持久化
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`src/agentkit/experts/orchestrator.py` `_check_divergence_and_insert_debates` — 插入辩论阶段后立即 `save_plan`。
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```python
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# Before
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debate = self._insert_debate_phase(plan, ph, topic, participants)
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if debate:
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await self._broadcast_event("plan_update", {...})
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# 未调用 save_plan — resume 看不到该 DEBATE 阶段
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# After
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debate = self._insert_debate_phase(plan, ph, topic, participants)
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if debate:
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await self._broadcast_event("plan_update", {...})
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if self._checkpoint is not None:
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try:
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await self._checkpoint.save_plan(plan) # resume 可见
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except Exception as e:
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logger.warning(f"Checkpoint save_plan (debate insert) failed: {e}")
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```
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### P2 #8：resume 不恢复 _debate_count
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`src/agentkit/experts/orchestrator.py` `resume()` — 统计恢复计划中的 DEBATE 阶段数。
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```python
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# Before
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# resume() 不触碰 _debate_count，恢复后归零，MAX_DEBATES 可被绕过
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# After
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self._debate_count = sum(
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1 for ph in plan.phases if ph.phase_type == PhaseType.DEBATE
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)
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```
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### P2 #9：循环检测状态不自动重置
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`src/agentkit/core/react.py` — 在 `execute()` 与 `execute_stream()` 入口调用 `self.reset()`。
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```python
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# Before
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async def execute(self, ...):
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effective_compressor = ... # 直接进入逻辑，_loop_window 残留上一会话
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# After
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async def execute(self, ...):
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self.reset() # 清空 _loop_window、_loop_corrected
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effective_compressor = ...
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```
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### P2 #11：resume 不处理 FAILED 阶段
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`src/agentkit/experts/orchestrator.py` `resume()` — 追踪 `failed_phase_ids` 并恢复 FAILED 状态。
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```python
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# Before
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for cp in checkpoints:
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if cp.phase_status == "completed":
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completed_phase_ids.add(cp.phase_id)
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# FAILED 阶段被忽略，恢复后变 PENDING，会被重跑
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# After
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failed_phase_ids: set[str] = set()
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for cp in checkpoints:
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if cp.phase_status == "completed":
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completed_phase_ids.add(cp.phase_id)
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if cp.phase_result:
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phase_results[cp.phase_id] = cp.phase_result
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elif cp.phase_status == "failed":
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failed_phase_ids.add(cp.phase_id)
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for ph in plan.phases:
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if ph.id in completed_phase_ids:
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ph.status = PhaseStatus.COMPLETED
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...
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elif ph.id in failed_phase_ids:
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ph.status = PhaseStatus.FAILED # 不再重跑
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```
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### P2 #13：checkpoint.clear() 从不调用
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`src/agentkit/experts/orchestrator.py` — 成功路径在 `team_dissolved` 事件后清理 checkpoint。
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```python
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# Before
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await self._broadcast_event("team_dissolved", {"team_id": self._team.team_id})
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return {"status": "completed", ...} # checkpoint 永不清理
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# After
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await self._broadcast_event("team_dissolved", {"team_id": self._team.team_id})
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if self._checkpoint is not None:
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try:
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await self._checkpoint.clear(plan.id)
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except Exception as e:
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logger.warning(f"Checkpoint clear failed: {e}")
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return {"status": "completed", ...}
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```
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### P2 #14：_offload_result 对非字符串内容崩溃
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`src/agentkit/experts/orchestrator.py` `_offload_result` — 入口加 `str()` 守卫。
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```python
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# Before
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def _offload_result(self, content: str, ref_key: str) -> dict[str, Any]:
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summary = content[: self._OFFLOAD_SUMMARY_LIMIT] # content 若为 dict/None → TypeError
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# After
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def _offload_result(self, content: str, ref_key: str) -> dict[str, Any]:
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if not isinstance(content, str):
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content = str(content) if content is not None else ""
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summary = content[: self._OFFLOAD_SUMMARY_LIMIT]
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```
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## Why This Works
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各修复都针对具体根因，而非表面症状：
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- **默认值回归（P0 #1）**：U5 渐进式技能加载引入 `disclosure_level` 时，默认值设为 0（概要模式）是新功能的"安全默认"，但忽略了既有技能契约——它们从未声明该字段，期望全量加载。改为 1 后，未显式声明的技能保持原有全量行为，只有显式选择概要模式的技能才降级。这是"新字段默认值应保持既有契约"的典型教训。
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- **标识符错配（P0 #2）**：`TeamPlan.id` 是自动生成的 UUID，与外部 `task_id` 是不同命名空间。resume 端点假设二者相等，导致 checkpoint 永远查不到。引入显式 `plan_id` 参数并保留 `task_id` 回退，既支持新调用方也兼容旧路径。并发 Lock 则防止同一计划被两个请求同时恢复造成状态错乱。
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- **格式契约不匹配（P1 #3、#4）**：中间件与核心数据类分属不同模块，中间件按"猜测"的格式/属性名访问结果。修复方式是显式兼容两种格式（dict + dataclass）并核对真实属性名。根因是跨模块契约未文档化——`ReActStep`/`ReActResult` 的字段定义应作为中间件的契约源。
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- **状态卸载未回读（P1 #5）**：U4 把大产出卸载到 SharedWorkspace，内存只留 500 字符摘要。但综合阶段仍直接读 `result["content"]`（即摘要），未解析 `_offloaded` 标记。修复复用了已有的 `_read_dependency_output()`，该函数本就为解析 offloaded 内容而存在——综合阶段只是漏调。
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- **去重数据结构选型（P1 #6）**：list 语义是"追加"，而 checkpoint 的语义是"同一阶段最新状态覆盖旧状态"。改用 dict keyed by phase_id 后，重复 save 自然覆盖，`list_checkpoints` 返回唯一阶段集合。
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- **动态修改需重新持久化（P1 #7）**：`save_plan` 只在流水线启动前调用一次，但 `_check_divergence_and_insert_debates` 会在执行中修改 `plan.phases`。resume 依赖持久化的 plan 重建，看不到动态插入的阶段就会丢失辩论环节。在每次动态修改后立即 `save_plan` 保证 resume 看到的是最新计划。
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- **状态恢复完整性（P2 #8、#11）**：resume 是"状态重建"，必须重建所有运行时状态——包括 `_debate_count`（限额计数器）和 FAILED 阶段状态。只恢复 COMPLETED 而忽略 FAILED，等于把已失败阶段当作待执行，违背了"resume 应精确还原崩溃前状态"的契约。
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- **跨会话状态隔离（P2 #9）**：`ReActEngine` 实例可能被复用（如 AgentPool），`_loop_window` 是实例级状态。若不在每次 execute 入口重置，上一会话的工具调用哈希会污染新会话的循环检测。`reset()` 方法本就存在，只是未被调用——这是"有清理方法但未接入生命周期"的常见漏洞。
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- **资源生命周期（P2 #13）**：checkpoint 是临时断点数据，成功完成后即失去价值。7 天 TTL 是兜底，但主动清理避免在 TTL 窗口内累积无意义数据。失败路径不清理，以便 resume 重试。
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- **类型守卫（P2 #14）**：`_offload_result` 的类型注解写的是 `str`，但实际调用方可能传入 dict 或 None。Python 运行时不强制注解，`len()` 与切片对非字符串直接报错。入口 `str()` 守卫是最低成本的防御，符合"信任边界处做校验"的原则。
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## Prevention
|
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||||
1. **新字段默认值须保持既有契约**：为既有数据类新增字段时，默认值必须让未声明该字段的旧代码保持原有行为。U5 的 `disclosure_level` 应默认为"全量加载"，而非新功能的"安全模式"。审查时对任何"新增字段改变既有行为"的默认值保持警惕。
|
||||
|
||||
2. **跨模块契约显式化**：中间件访问核心数据类（`ReActStep`、`ReActResult`）的字段时，应以数据类定义为契约源，而非凭命名习惯猜测。建议在数据类定义处注释"被中间件依赖的字段"，或在中间件处注释"契约来源：core/react.py: ReActResult"。
|
||||
|
||||
3. **resume 是完整状态重建**：任何运行时计数器（`_debate_count`）、阶段状态（含 FAILED）、动态修改（插入的 DEBATE）都必须在 resume 中还原。审查 resume 逻辑时，逐项核对"execute 期间会修改哪些状态"，确保每项都有对应的恢复路径。
|
||||
|
||||
4. **动态修改后立即持久化**：凡是在执行过程中修改了需被 resume 重建的对象（如 `plan.phases`），修改后必须立即 `save_plan`。不要依赖"结束时统一保存"——崩溃可能发生在保存之前。
|
||||
|
||||
5. **清理方法接入生命周期**：`reset()`、`clear()` 这类方法若存在，必须在对应生命周期入口（execute、完成、销毁）被调用。审查时搜索"定义了但未被调用的清理方法"。
|
||||
|
||||
6. **类型注解不是运行时保证**：Python 类型注解不强制，调用方可能传入任意类型。在 `len()`、切片、属性访问等对类型敏感的操作前，对来自外部（其他模块、LLM 产出、反序列化）的输入加 `isinstance` 守卫。
|
||||
|
||||
7. **审查 finding 逐条验证**：本次 14 条 finding 中 1 条为误报。批量审查时每条都需独立验证，避免按"看起来合理"就修改，引入回归。
|
||||
|
||||
8. **回归测试**：本次修复涉及的关键路径应有最小可运行检查——`disclosure_level` 默认值、resume 状态恢复、offloaded 内容回读、非字符串内容守卫。按项目规则（`python3 -m pytest tests/unit/ -x -q`）运行单元测试，确保上述路径不回归。
|
||||
|
||||
## Related Issues
|
||||
|
||||
- **上游计划**：[docs/plans/2026-06-24-004-feat-long-horizon-reliability-optimization-plan.md](file:///Users/Chiguyong/Code/Fischer/fischer-agentkit/docs/plans/2026-06-24-004-feat-long-horizon-reliability-optimization-plan.md) — U1-U7 实施计划，14 个 finding 均来自对该计划实现单元的代码审查
|
||||
- **相邻领域**：[docs/solutions/architecture-patterns/bitable-companion-service-security-reliability-patterns.md](file:///Users/Chiguyong/Code/Fischer/fischer-agentkit/docs/solutions/architecture-patterns/bitable-companion-service-security-reliability-patterns.md) — 不同模块（bitable 安全 vs 编排器可靠性），无内容重叠
|
||||
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Reference in New Issue