Agent Lifecycle Management

Complete guide to managing sub-agent and teammate lifecycle in Claude Code — health checks, idle detection, cleanup, retention, and the mandatory audit loop.

The Orchestration-First Principle

Claude should prefer to orchestrate rather than do work directly. When a task has multiple components or would benefit from specialized review:

1. Break the task into work units
2. Assign each to the best-fit agent type
3. Run agents in parallel where possible
4. Audit every agent's output
5. Clean up when done

Direct work is the fallback, not the default.

Agent Lifecycle States

                    ┌──────────┐
          ┌────────→│  active   │──────────┐
          │         └──────────┘           │
          │              │                 │
     spawn/resume    no output          completes
          │           >60s                 │
          │              ↓                 ↓
    ┌──────────┐   ┌──────────┐    ┌──────────┐
    │ spawning │   │   idle   │    │ completed│
    └──────────┘   └──────────┘    └──────────┘
                        │                 │
                   no response         collected?
                     >120s                │
                        ↓            yes/   \no
                   ┌──────────┐      ↓       ↓
                   │ stalled  │   released  orphaned
                   └──────────┘
                        │
                   terminate
                        ↓
                   ┌──────────┐
                   │ errored  │
                   └──────────┘

Health Check Protocol

When to Check

• During orchestration: Every 2 minutes
• After fan-out: Once all agents are spawned
• Before fan-in: Before collecting results
• On completion: Before producing final output

How to Check

For foreground agents: They return results when complete. No polling needed.

For background agents: Use SendMessage to check in:

SendMessage(to: "agent-id", message: "Status check: progress on your task?")

For agent teams: Teammates communicate via shared task list. Check TaskList for stale assignments.

Check-In Message Template

"Status check on your assigned task: [{task}].
Reply with one of:
- WORKING: {current activity}
- BLOCKED: {what's blocking}
- DONE: {summary}
- NEEDS_HELP: {what you need}"

Idle Detection & Cleanup

Detection Signals

Signal	Threshold	Action
No output	60s	Send check-in
No response to check-in	30s	Mark stalled
Stalled	180s	Terminate, reassign
Completed but uncollected	60s	Collect results
Errored	Immediate	Collect partial, log

Cleanup Procedure

cleanup_steps:
  1_identify:
    - List all agent IDs from spawn records
    - Check each agent's last known state
    - Identify: completed, idle, stalled, errored

  2_collect:
    - For completed agents: gather results
    - For errored agents: gather partial output + error
    - For stalled agents: attempt final check-in

  3_terminate:
    - Release completed agents (results collected)
    - Terminate stalled agents (no response after 3 min)
    - Terminate errored agents (unrecoverable)
    - Keep active agents running

  4_report:
    - Log cleanup summary
    - Report any lost work
    - Suggest task reassignment if needed

Token-Aware Cleanup

Track cumulative token usage across all agents:

Per-agent budget = total_budget / num_agents
If agent exceeds 150% of per-agent budget → flag for review
If total usage exceeds 80% of budget → switch to cheaper models
If total usage exceeds 95% → terminate non-essential agents

The Mandatory Audit Loop

Every agent's work must be audited before acceptance.

Audit Flow

Agent completes task
       ↓
Orchestrator spawns audit agent (code-reviewer or audit-reviewer)
       ↓
Audit agent reviews:
  - Completeness (did it do everything asked?)
  - Correctness (is the output right?)
  - Consistency (does it match project style?)
  - Security (any vulnerabilities introduced?)
  - Tests (are new things tested?)
       ↓
Audit verdict:
  PASS → accept, merge into deliverables
  PASS_WITH_NOTES → accept, log improvement notes
  FAIL → send back to original agent with specific fixes
           ↓
         Re-do (max 2 rework rounds)
           ↓
         Re-audit
           ↓
         If still fails → escalate to user

Audit Agent Selection

Original Agent	Audit Agent	Focus
Builder/Implementer	code-reviewer	Correctness, completeness
Test writer	code-reviewer	Test quality, coverage
Security reviewer	audit-reviewer (opus)	False positives, missed vectors
Researcher	audit-reviewer (opus)	Source quality, bias
Doc writer	code-reviewer	Accuracy, completeness
Infrastructure	security-reviewer	Misconfigs, blast radius

Audit Depth Levels

audit_levels:
  quick:
    checks: [completeness, correctness]
    model: haiku
    cost: low

  standard:
    checks: [completeness, correctness, consistency, security]
    model: sonnet
    cost: medium

  thorough:
    checks: [completeness, correctness, consistency, security, testing, documentation]
    model: opus
    cost: high

Agent Team Lifecycle

For Claude Code Agent Teams (Experimental)

Teams have additional lifecycle considerations:

team_lifecycle:
  spawn:
    - Lead session creates team
    - Lead defines task list with dependencies
    - Teammates join and claim tasks

  active:
    - Teammates self-coordinate via TaskList
    - Direct messaging via SendMessage
    - Lead monitors overall progress

  idle_detection:
    - Teammate hasn't claimed new task in 120s
    - Teammate's current task hasn't progressed
    - TeammateIdle hook fires

  cleanup:
    - Lead sends final status check to all teammates
    - Collect all teammate outputs
    - TeamDelete to dissolve team
    - Prune any worktrees created

  audit:
    - Lead reviews each teammate's work
    - Cross-check: teammate A audits teammate B's output
    - Final synthesis by lead

Cross-Audit Pattern

In teams of 3+, use cross-auditing:

Agent A completes → Agent B audits A's work
Agent B completes → Agent C audits B's work
Agent C completes → Agent A audits C's work

This distributes audit load and provides diverse perspectives.

Retention Policies

When to Keep an Agent Alive

• Pipeline stage: Agent is between steps, waiting for upstream input
• Expensive context: Agent has loaded large codebase context that would cost tokens to rebuild
• Follow-up planned: User may ask for iterations on agent's work
• Background monitoring: Agent is running periodic checks

When to Terminate

• Task complete, results collected: No further value
• Idle >3 minutes, no planned follow-up: Wasting potential tokens
• Stalled despite check-ins: Likely stuck, won't recover
• Budget exceeded: Cost ceiling reached
• Circular work: Agent is repeating itself

Best Practices

1. Always track agent IDs — maintain a mental or written registry of every agent spawned
2. Collect results immediately — don't let completed agents sit uncollected
3. Use background mode sparingly — foreground is easier to manage
4. Match model to task — haiku for research, sonnet for implementation, opus for architecture/audit
5. Set max_turns — prevent agents from running indefinitely
6. Use worktree isolation — for agents that write code, prevent conflicts
7. Audit before accepting — never skip the second-round review
8. Clean up before finishing — no orphaned agents when orchestration ends