s15_integrated_harness

s15: Integrated Harness — Many Mechanisms, One Loop

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s01 → ... → s13 → s14 → s15 → s16 → s17

"Many mechanisms, one loop" — tools, permissions, memory, tasks, teams, and plugins all hang off the same while True.

Harness layer: Integration — put the mechanisms used by this example into one runnable system.

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Problem

The earlier chapters keep separate mechanisms in separate runnable examples. This chapter connects the mechanisms needed by the integrated runtime.

A long-running coding agent needs all of these at once:

• tool dispatch and permission boundaries
• hook extension points
• todo planning and task graphs
• skills, memory, and runtime system prompt assembly
• compaction and error recovery
• background tasks and cron scheduling
• teams, protocols, and IDLE task claiming
• task-bound worktrees
• MCP external tool integration

S15 does not introduce another isolated mechanism. It shows where the existing mechanisms enter the model loop and how their events return to the same conversation.

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Solution

S15 does not introduce a new mechanism. It connects the components from the earlier chapters in one integrated harness:

user input
  → UserPromptSubmit hooks
  → cron/background notification injection
  → context compact
  → memory + skills + MCP state assemble the system prompt
  → LLM
  → has tool_use block?
      no  → Stop hooks → return
      yes → PreToolUse hooks + permission
          → TOOL_HANDLERS / MCP handlers / background dispatch
          → PostToolUse hooks
          → tool_result / task_notification back to messages
          → next round

The loop keeps the same structure: call the model, check whether the response contains a tool_use block, execute tools, and append results to messages. The presence of a tool_use block decides whether tool execution continues.

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Where Each Component Sits

Position	Component	Role
Around user input	UserPromptSubmit hooks	Log, inject, or audit user input
Before LLM	cron queue	Inject scheduled prompts into messages
Before LLM	background notifications	Inject completed background work as <task_notification>
Before LLM	compaction pipeline	Budget large outputs, trim history, compact old tool results, summarize when needed
Before LLM	memory / skills / MCP state	Assemble the system prompt so the model sees current capabilities and long-term context
LLM call	error recovery	Retry 429/529, escalate max_tokens, compact on prompt-too-long
Before tool execution	PreToolUse hooks + permission	Block dangerous commands, out-of-bounds writes, destructive MCP tools
Tool dispatch	assemble_tool_pool	Assemble built-in tools and dynamic MCP tools
During tool execution	background dispatch	Move explicitly marked bash work into a daemon thread and return a placeholder result
After tool execution	PostToolUse hooks	Large-output warnings, logs, post-processing
Back to loop	tool_result	One tool_result per tool_use, then the next model round
No tool_use this round / on stop	Stop hooks	Stats, cleanup, audit

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What code.py Contains

Tools and Dispatch

The built-in tool pool contains 26 tools:

bash, read_file, write_file, edit_file, glob
todo_write, task, load_skill, compact
create_task, update_task, list_tasks, get_task, claim_task, complete_task
schedule_cron, list_crons, cancel_cron
spawn_teammate, list_teammates, send_message
request_shutdown, request_plan, review_plan
create_worktree
connect_mcp

assemble_tool_pool() assembles these every round:

BUILTIN_TOOLS + connected MCP tools
BUILTIN_HANDLERS + mcp__server__tool handlers

After connect_mcp("docs"), the next round exposes tools like mcp__docs__search.

Permissions and Hooks

Permission is not hardcoded into the tool execution line. It is a PreToolUse hook:

blocked = trigger_hooks("PreToolUse", block)
if blocked:
    results.append(tool_result(block.id, blocked))
    continue

That means permission, logging, and audit logic all attach to the same hook point. Lead tools, one-shot subagent tools, and teammate tools all pass through PreToolUse; an allowed call then runs PostToolUse after its handler.

The policy does not trust an MCP server's own description as authorization. The host owns a small exact allowlist for known read-only calls; every other MCP tool asks the user. File tools are denied outside WORKDIR, and every bash command asks before execution. Only the foreground user turn may open an interactive approval prompt; asynchronous turns fail closed instead of competing with the main CLI for stdin.

Planning and Tasks

S15 keeps two planning layers:

• todo_write: lightweight plan for the current session, kept in memory
• task graph: cross-session, dependency-aware, claimable task files under .tasks/task_*.json

The first keeps a single agent from drifting. The second supports team coordination.

They share an intent, not an implementation: todo_write replaces one session checklist, while task records have stable IDs and individual lifecycle updates. The separate task tool below means "dispatch one isolated subagent"; it is not the Task System.

Task graph construction remains two-phase in the integrated host: the Lead creates all task nodes first, then calls update_task with the runtime IDs returned by create_task. Teammates receive only list, claim, and complete operations, so dependency structure is fixed by the Lead before work is distributed.

Subagents and Teams

S15 has two kinds of delegation:

• task: one-shot subagent. It uses an isolated messages[], discards intermediate context, and returns only a final summary.
• spawn_teammate: persistent teammate thread. When given a ready task_id, the runtime claims it before the thread starts; without one, the teammate can wait in IDLE for later work. A teammate without an assignment cannot use file or Shell tools. It follows WORK → result → IDLE without a fixed tool-round cap; model or dispatch failures emit an error, and thread cleanup releases an unfinished assignment back to the task board. It drains its inbox before every model call, so direct messages and shutdown requests cannot wait behind an unbroken tool-use sequence. While idle it waits for MessageBus delivery first, then scans ready tasks only after the wait times out and atomically claims at most one.

After spawning a teammate, Lead ends the current turn instead of repeatedly querying its status inside the model loop. A team event in Lead's mailbox makes the runtime start the next turn.

One-shot subagents solve context isolation. Persistent teammates solve long-running parallel collaboration.

Memory, Skills, and Prompt

S15 reuses the s09 memory runtime directly. Before each model call, it reads the .memory/MEMORY.md catalog, selects records relevant to the current request, and passes their contents to assemble_system_prompt(context). At the end of the turn, extract_memories() keeps information that can help in later sessions; when new records are stored, consolidate_memories() runs next.

The same system prompt also includes identity, tool guidance, the workspace, the skills catalog, and connected MCP servers. Skills contribute only their catalog; load_skill(name) loads full content on demand.

Compaction and Recovery

Before the LLM call, S15 runs the compaction pipeline:

tool_result_budget → snip_compact → micro_compact → compact_history

The model call is wrapped with recovery:

• 429: exponential backoff retry
• 529: exponential backoff, optionally switch to fallback model after repeated failures
• max_tokens: raise max tokens, then request continuation
• prompt too long: reactive compact and retry

Background and Cron

When a bash call sets run_in_background=true, the main loop returns a placeholder without waiting for the command:

should_run_background → start_background_task → placeholder tool_result
background done → task_notification → next round injects messages

Only explicitly marked bash calls enter the background path. A non-zero exit or worker exception produces a failed notification. Each shell runs in its own process group, which the runtime stops when the command or Agent process ends through the normal or SIGTERM path. A process that creates another session can leave that group.

The cron scheduler runs as a daemon thread and checks once per second. A durable one-shot job is persisted as pending_delivery before entering the queue and remains there until the model call containing its prompt succeeds; a failed call restores it to the queue, and a restart queues it again. Delivery is therefore at-least-once. The CLI watches cron_queue, Lead's inbox, and terminal background work; any of them can wake one automatic agent turn.

Worktree and MCP

The task-scoped worktree behavior inherited from s13 manages working directories:

• a pending, unowned task may remain in the main workspace or be bound by create_worktree(name, task_id) to a separate branch and directory
• creation prevalidates the task, name, path, branch, and Git registry; a failed Git command is reconciled against the registry and branch state, and any partial checkout remains unbound and preserved for manual recovery
• an idle teammate atomically claims one ready task; the assignment records both task_id and its effective cwd
• Lead can also pass a ready task_id to spawn_teammate; the thread starts only after the claim succeeds
• all teammate file tools use that cwd; only the owning teammate can complete the task, and the assignment stays selected until that model turn ends
• removal stays in the host-side remove_worktree() helper. The model cannot call it. The user or host first checks task ownership, assignment leases, background work, and Git state; destructive removal requires separate user confirmation

The worktree changes tool default directories. It separates working copies; it is not a sandbox, and process-group cleanup does not contain a process that starts another session. This is why deletion remains host-owned.

Claiming or releasing a Task changes the assignment version and invalidates an old plan approval. An ordinary send_message only delivers text; it changes neither the Task identity nor the plan state.

MCP owns external capability:

• connect_mcp(name) connects a mock server
• assemble_tool_pool() assembles MCP tools and rejects normalized name collisions
• tool names use mcp__server__tool

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Changes from s14

Scope	s14 MCP	s15 Integrated Harness
built-in tools	6	25
external tools	connected MCP tools	the same dynamic MCP path and host policy
local mechanisms	S04 tools, hooks, permission, MCP	todo, subagent, skills, compaction, memory, task graph, background bash, cron, teams, and worktrees
event sources	user input and tool results	user input, tool results, cron prompts, background notifications, and team events

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Try It

cd learn-claude-code
python s15_integrated_harness/code.py

Try:

1. Inspect this repository and tell me which Python files matter most.
2. Search the connected documentation for agent loop guidance.
3. Refactor the authentication module and login page in parallel in separate worktrees. Show me each plan before editing.
4. Remind me about the meeting in 3 minutes.
5. Install the dependencies in the background while you read README.md.

Watch for:

• whether each tool call passes through hooks/permission
• whether MCP tools appear on the next round after connect_mcp
• whether a bash call with run_in_background=true returns a background placeholder
• whether cron automatically reminds you when the time arrives
• whether teammates submit plans and pause before approval
• whether an idle teammate atomically claims only one ready task
• whether every teammate file tool switches to the claimed task's cwd
• whether completion keeps the task cwd through the rest of the turn and releases it at IDLE

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Next

s16 Workflow Runtime adds a Workflow tool to this host. A workflow keeps a fixed orchestration path in code and records progress so the same run can resume.