spawn-implementation-agents

To conserve agent context during large codebase implementation, orchestrate specialized sub-agents in a five-phase model that keeps the main agent under 40k tokens per phase by offloading analysis, coding, testing, verification, and documentation tasks to sub-agents.

The five-phase orchestration pattern for AI agents is a structured workflow model consisting of Analysis, Implementation, Testing, Verification, and Documentation phases, using parallel analysis and sequential verification to manage token budgets and enable safe data handoffs between agents.

You can orchestrate parallel sub-agents for codebase analysis by spawning dedicated agents to analyze different parts simultaneously, requiring them to return structured summaries instead of raw files to reduce main-context load and keep token usage within the 40k limit per phase.

You need sub-agent orchestration for implementation workflows when working across large codebases where end-to-end tasks involving analysis, coding, testing, verification, and documentation threaten to exceed the main agent's context window capacity and degrade performance.

Yes, sub-agent orchestration works for end-to-end testing and verification workflows by applying sequential verification phases after parallel analysis and implementation, ensuring safe data handoffs between agents while maintaining a strict 40k token budget per phase.

A limitation of the five-phase agent orchestration model is the overhead of managing safe data handoffs between sub-agents, requiring strict adherence to sequential verification and parallel execution guidelines to prevent context overflow beyond the 40k token budget.