Guide

AI SDLC: How AI Agents Change the Software Development Lifecycle

Last updated: November 2, 2026

An AI SDLC is a software development lifecycle where AI agents do much of the work in each phase. People still set the goal, approve the plan and decide what ships. When the agents act on their own between those checkpoints, it is often called an agentic SDLC.

The phases do not change much. What changes is who does the work in each one, and how fast a phase can finish.

The SDLC, phase by phase, with AI agents

Requirements. An agent interviews the product owner, writes down the answers and points out what is missing. People approve the result. This is the most important checkpoint in the whole cycle.

Design. Agents draft the architecture notes, data models and screens from the approved requirements. A person reviews them before any code is written.

Implementation. Coding agents such as Claude Code, Codex and Copilot write the code and the tests, one task at a time, each in its own branch.

Testing. Agents write and run tests. The results must come from a real test run, not from the agent's own summary of what it did.

Review and release. A second AI provider reviews each change, and a person merges it. Release and production access stay with the team.

AI SDLC vs. AIDLC

The two names describe the same shift. AI SDLC is the general term: the SDLC you know, with AI agents added to each phase. AIDLC (AI-DLC) is a specific method, named by AWS, that regroups the work into three phases: inception, construction and operations.

If your team already has an SDLC with its own gates, start with AI SDLC: keep your phases and let agents take over steps inside them. If you are starting fresh, AIDLC gives you a ready structure.

What a good AI SDLC framework needs

  • A written spec. Agents build what they are told. A vague prompt gives vague code.
  • Human gates. Approval after requirements, after the plan, and before the merge.
  • Small tasks. Each task is small enough for one agent to finish and one person to review.
  • Traceability. Every pull request links back to the requirement that caused it.

Common mistakes

Skipping requirements. Teams give agents a ticket title and hope for the best. The agent fills the gaps with guesses, and the review takes longer than writing the code would have.

Trusting the summary. An agent that says "all tests pass" may not have run them. Re-run the checks outside the agent.

Too many tools. One agent for planning, another for code, a third for review, each with its own rules. Without one shared plan, they drift apart.

Running an AI SDLC with nTra

nTra is a spec-driven product engineering platform: every task your AI coding agents build traces back to a requirement your business signed off. It covers the cycle from the first requirement to the pull request. Discovery writes the brief with you, and nothing is planned until you approve it.

nTra then breaks the brief into documents, screens and sized tasks. Each task runs through the coding agent your team already uses. nTra re-runs the checks and opens a pull request, and your team merges it.

See how nTra works, or read about spec-driven development.

Related guides