Framework
AI-Native SDLC
Apply the proven software development lifecycle (epic → requirement → atomic task) to AI agent workflows. Structured decomposition puts agents on rails and produces working software instead of context-drifted hallucinations.
How it works
- 1The failure mode: give an AI agent vague intent ('make a multi-tenant platform') and it confidently invents an architecture that may be technically coherent and completely incompatible with what you need.
- 2The same problems that made unstructured human development fail (unclear scope, missing edge cases, no acceptance criteria) make AI agent development fail faster — because agents don't ask for clarification, they hallucinate answers.
- 3Epic: the large body of work. Requirement: a single feature that satisfies part of the epic. Atomic task: the smallest executable unit — one task the agent can complete, test, and lint in a single turn before moving to the next.
- 4The single-turn standard: an atomic task is sized so the agent can complete it without context drift. If the agent needs to make a judgment call mid-task about something not in the spec, the task is not atomic enough.
- 5Parallelization: once requirements are broken into atomic tasks, Git worktrees allow multiple agents to work on multiple requirements simultaneously — six agents building six features on six branches of the same codebase at once.
Hear it on the podcast
Vibe Coders RejoiceListen to the episodeMore from this episode
Context Grounding for AgentsAI coding agents operating without codebase knowledge generate generic, architecturally incompatible software. Ground agents in five types of codebase documentation before generating any requirements or tasks.Intent-First DevelopmentStart with expressed intent in plain English. The planning layer converts raw founder intent into agent-ready specifications — preserving the momentum and dopamine of idea exploration while producing the precision execution requires.
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