Spawn Implementation Agents

Orchestrate specialized agents during implementation to keep main agent context under 40k tokens per phase.

The Problem

Without agents, implementing a phase uses ~92k tokens in main agent:

• Read plan & changelog: 15k
• Read existing code files: 30k
• Find usage patterns: 15k
• Write implementation: 10k
• Write tests: 10k
• Run verification: 10k
• Update changelog: 2k

This approaches the 200k context limit and risks compaction.

The Solution

Use agents to isolate heavy operations:

• Main agent: 38k tokens (plan + changelog + summaries + code writing)
• Sub-agents: 60k tokens total (in isolated contexts)
• Total system: 98k tokens (50% safety margin)

5-Phase Orchestration Pattern

Phase 1: Analysis (Parallel)

Spawn simultaneously to gather context:

Task(subagent_type="workflows:codebase-analyzer",
     prompt="Analyze existing auth system architecture.
     Focus on handler pattern, middleware usage, error handling.
     Return 2-3k summary with key patterns and file:line references.")

Task(subagent_type="workflows:codebase-pattern-finder",
     prompt="Find similar implementations of authentication handlers.
     Return 3k of concrete examples showing handler pattern, validation, errors.")

Task(subagent_type="workflows:thoughts-analyzer",
     prompt="Extract insights from changelog.md about previous phase learnings.
     Return 2k of key deviations and discoveries that affect this phase.")

Wait for all three. Main agent receives ~8k of summaries.

Phase 2: Implementation (Main Agent)

Main agent writes code using summaries:

• Has patterns from codebase-pattern-finder
• Understands architecture from codebase-analyzer
• Knows previous deviations from thoughts-analyzer
• Writes implementation: 10k tokens
• Total so far: 15k (plan/changelog) + 8k (summaries) + 10k (code) = 33k

Phase 3: Testing (Sequential)

Spawn test writer:

Task(subagent_type="workflows:test-writer",
     prompt="Generate tests for AuthHandler following patterns in testing.md.
     Test functions: Login(), Logout(), ValidateToken().
     Return test code only, ~3k tokens.")

Main agent receives test code, integrates it. Total: 36k

Phase 4: Verification (Sequential)

Spawn verifier:

Task(subagent_type="Bash",
     prompt="Run verification commands from plan.md:
     - make test
     - make lint
     - make build
     Return concise summary: ✅ passed or ❌ failed with key errors only.")

Main agent receives pass/fail + errors. Total: 38k

Phase 5: Documentation (Main Agent)

Update changelog.md: 2k tokens. Final total: 40k

Token Budget Comparison

| Activity | Without Agents | With Agents | Savings | |----------|----------------|-------------|---------| | Read plan & changelog | 15k | 15k | 0k | | Understand existing code | 30k | 3k | 27k | | Find patterns | 15k | 3k | 12k | | Write implementation | 10k | 10k | 0k | | Write tests | 10k | 3k | 7k | | Run verification | 10k | 2k | 8k | | Update changelog | 2k | 2k | 0k | | TOTAL | 92k | 40k | 52k |

Guidelines

When to spawn in parallel:

• Analysis phase (codebase-analyzer + pattern-finder + thoughts-analyzer)
• Independent lookups (finding multiple unrelated examples)
• Reading multiple unrelated files

When to spawn sequentially:

• Test writing (needs implementation to be done first)
• Verification (needs tests to be written first)
• Operations that depend on previous results

What agents return:

• Summaries, not raw data (2-5k tokens each)
• Key patterns, not all files (concrete examples only)
• Pass/fail + errors, not full output (1-2k tokens)

Benefits

• 60% token reduction per phase in main agent
• Larger phases possible: 5-8 files instead of 3-5
• Complex integrations supported: Agents find patterns
• Large files OK: Agents handle reading (>2000 lines)
• Safety margin: 100k tokens remaining in system

Important Notes

• Main agent NEVER reads large files directly
• Main agent orchestrates, sub-agents execute
• Summaries are compressed, not exhaustive
• This is guidance, not automation - user still in control