Agentic SDLC
A software development lifecycle in which AI coding agents are active participants in producing, testing, and deploying code — not just tools used by human developers. In an agentic SDLC, agents don't wait for prompts; they propose changes, run verification, iterate on failures, and advance work autonomously within defined boundaries.
Why It Matters
Traditional SDLCs assume humans perform every action: writing code, running tests, reviewing pull requests, deploying releases. When AI agents enter the workflow as participants rather than assistants, the entire lifecycle must adapt. Velocity increases by orders of magnitude, but so does the potential for undetected errors compounding at machine speed. Without governance designed for agentic participation, organizations either throttle agents into irrelevance or let them ship unchecked work that erodes trust.
The agentic SDLC reframes the development process around the question: what decisions require human judgment, and what can agents handle autonomously with verification?
How It Works
- Agents as first-class participants — AI coding agents own execution of well-defined tasks: implementing features, writing tests, fixing bugs, optimizing performance. They operate within declared boundaries, not open-ended prompts.
- Verification replaces inspection — instead of human code review on every change, automated verifiers confirm correctness. Humans review architecture decisions and edge cases, not routine implementations.
- Three-layer governance — human decisions at the top (intent, accountability), gate-layer judgment in the middle (security, compliance, domain expertise), and autonomous loop execution at the bottom (build, test, verify, deploy).
- Persistent state across sessions — agents maintain context about what was tried, what failed, and what succeeded. Work compounds across sessions rather than restarting from zero.
- Escalation paths — when agents encounter ambiguity beyond their authority, they escalate to humans rather than guessing. The system knows its own boundaries.