"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.

Problem

The first 16 chapters add one mechanism at a time so each boundary stays visible. This chapter connects them in one 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, autonomous claiming
task-bound worktrees
MCP external tool integration

The hard part is not piling up features. The hard part is seeing where each mechanism belongs around the loop. S17 is the integration checkpoint: the mechanisms retained by this runnable example are placed into one harness. S18 extends it with workflow orchestration; s19 uses a smaller loop to study goal closure on its own.

Solution

S17 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.

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 slow 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

What code.py Contains

Tools and Dispatch

The built-in tool pool contains 24 tools:

bash, read_file, write_file, edit_file, glob
todo_write, task, load_skill, compact
create_task, list_tasks, get_task, claim_task, complete_task
schedule_cron, list_crons, cancel_cron
spawn_teammate, 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

S17 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.

Subagents and Teams

S17 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. 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.

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

Memory, Skills, and Prompt

assemble_system_prompt(context) assembles each round from:

identity and tool guidance
workspace
skills catalog
.memory/MEMORY.md
connected MCP servers

Skills only put their catalog into the system prompt. Full content is loaded on demand through load_skill(name).

Compaction and Recovery

Before the LLM call, S17 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

Slow bash work does not block the main loop:

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

Only bash can enter the background path. A non-zero exit or worker exception produces a failed notification instead of a false success. 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. That cleanup covers the original group; a process that creates another session can escape it.

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 s15 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
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.

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

Changes from s16

Component	s16 MCP	s17 Integrated Harness
tool pool	built-in + MCP	built-in + MCP, with s01-s15 mechanisms restored
permission	outside s16's focus	runs inside `PreToolUse` hook
hooks	outside s16's focus	UserPromptSubmit / PreToolUse / PostToolUse / Stop
todo	outside s16's focus	`todo_write` + reminder
skill	outside s16's focus	catalog in system prompt + `load_skill`
compact	outside s16's focus	pre-LLM compaction + `compact` tool + reactive compact
error recovery	simple try/except	retry / max_tokens / prompt too long
background	background bash + notifications	same lifecycle, with permission hooks in the execution path
cron	daemon scheduler + durable jobs	same scheduler inside the integrated event loop
multi-agent	inherited from s15	preserved with atomic task ownership and task-scoped `cwd`
worktree	optional task binding	model creates; host reviews and removes
MCP	introduced	preserved as part of the integrated tool pool

Try It

cd learn-claude-code
python s17_integrated_harness/code.py

Try:

Inspect this repository and tell me which Python files matter most.
Search the connected documentation for agent loop guidance.
Refactor the authentication module and login page in parallel in separate worktrees. Show me each plan before editing.
Remind me about the meeting in 3 minutes.
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 slow operations return 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

The End Is the Beginning

From s01 to s17, the code gets more capable, but the core remains unchanged:

while True:
    response = LLM(messages, tools)
    if not has_tool_use(response.content):
        return
    results = execute_tools(response.content)
    messages.append(tool_results)

A mature harness gets its complexity from coordination around the model. The model chooses actions; the harness organizes the environment, tools, permissions, memory, teams, and external capabilities.

This is the course's integration checkpoint: many mechanisms, one loop.

Next: s18 Workflow Runtime — when the orchestration shape is fixed, move it out of chat turns and into deterministic, resumable code.