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

Harness layer: Comprehensive — put the mechanisms from s01-s18 into one runnable system.

Problem

The first 18 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
worktree isolation
MCP external tool integration

The hard part is not piling up features. The hard part is seeing where each mechanism belongs around the loop. S19 is the integration checkpoint: every earlier component is placed back into one harness before s20-s21 add orchestration and goal closure around it.

Solution

S19 does not introduce a new mechanism. It connects the components from the earlier chapters in one complete 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 26 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, remove_worktree, keep_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. After execution, PostToolUse hooks run.

Planning and Tasks

S19 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

S19 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. The runtime delivers MessageBus events, and the teammate scans the task board while idle so it can claim work autonomously.

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, S19 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

The cron scheduler runs as a daemon thread and checks once per second. The CLI watches cron_queue; when a job fires, it injects [Scheduled] ... and runs one agent turn automatically.

Worktree and MCP

Worktree isolation owns directories:

create_worktree(name, task_id) creates an isolated branch and directory
the task worktree field binds a task to that directory
when a teammate claims a task with a worktree, its bash/read/write tools run in that directory

MCP owns external capability:

connect_mcp(name) connects a mock server
assemble_tool_pool() assembles MCP tools into the tool pool
tool names use mcp__server__tool

Changes from s18

Component	s18	s19
tool pool	built-in + MCP	built-in + MCP, with s01-s17 tools restored
permission	outside s18's scope	runs inside `PreToolUse` hook
hooks	outside s18's scope	UserPromptSubmit / PreToolUse / PostToolUse / Stop
todo	outside s18's scope	`todo_write` + reminder
skill	outside s18's scope	catalog in system prompt + `load_skill`
compact	outside s18's scope	pre-LLM compaction + `compact` tool + reactive compact
error recovery	simple try/except	retry / max_tokens / prompt too long
background	outside s18's scope	slow-operation thread + task notification
cron	outside s18's scope	daemon scheduler + durable jobs
multi-agent	kept	kept; teammates use basic tools in isolated directories
worktree	kept	kept
MCP	new	kept as part of the final tool pool

Try It

cd learn-claude-code
python s19_comprehensive/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 isolated 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 teammates can claim tasks after plan approval
whether teammates switch to the bound worktree directory

The End Is the Beginning

From s01 to s19, 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: s20 Workflow Runtime — when the orchestration shape is fixed, move it out of chat turns and into deterministic, resumable code.