AI SDLC: the AI software development lifecycle for teams
Coding agents write the code. The work moves to deciding what to build, writing it down and checking the result. Feature1 is the product operating system where your team does that work, and your agent builds from it.
What is the AI SDLC?
The AI software development lifecycle (AI SDLC), also called the AI-native or AI-driven SDLC, is a way of building software in which coding agents generate the code from written intent, and people decide what should exist, write the acceptance criteria and verify what was built. Writing code stops being the slow step; deciding and verifying become the job.
As a framework, the AI SDLC has four steps:
Decide
Which problem, for which customer, and why now.
Specify
The feature, its user stories and acceptance criteria.
Generate
The coding agent builds from the written intent.
Verify
A person checks the result against the criteria.
Where AI SDLC playbooks stop
Most AI SDLC diagrams start with the intent already written. In a real team it is not, and that is where the time goes:
- Developers prompt the agent from a ticket title, and it builds what the ticket said, not what was meant.
- Nobody can say which customer or goal a feature serves.
- The agent rebuilds things the product already does.
- After release, nobody checks whether the feature is used.
Feature1 is the AI SDLC platform for the step before and after the agent: the strategy, roadmap and acceptance criteria going in, and adoption and errors coming back. We wrote the long version in The AI-native SDLC, with the part it leaves out.
New to the AI SDLC? Adopt it with your team
Book a proof of concept (POC). We set the AI SDLC up with your PM and one developer and stay with you, hands on, until two real features are delivered.
- Week 1. NDA, read-only GitHub access, then the product strategy and roadmap with your PM in Feature1. The first two features are specified with acceptance criteria.
- Week 2. Your developer builds both features with their own coding agent, reading the criteria over MCP. We review against the criteria together and ship.
Proof of concept (POC)
$500 paid only when the two features ship
- One to two weeks, your PM and one developer
- Your developer writes the code; we guide the process
- You keep the strategy, roadmap and specs
Already using coding agents? Give them intent worth building from
Your developers keep their agents and their repository workflow. Feature1 becomes where the intent is written and checked.
- Strategy, roadmap and acceptance criteria written with the PM, not in a prompt.
- Claude Code, Codex or Cursor reads the approved criteria over MCP.
- Feature1 reads the repository read-only, so specs start from what the product already does.
- Adoption and errors come back to the feature after release.
7-day free trial
Free no credit card
Sign up, invite your team and write the intent for your next feature. Connect your coding agent when you are ready.
Start the free trialTeams already working this way
- SatoriXR. Delivery got faster and deciding became the constraint. They hired a second product manager, not a third developer.
- Quantem. A junior developer built an MCP layer in one day from written acceptance criteria. The context took four to five days; the build took one.
- Jarshare. A Replit store with one unintegrated server file became a real product once its next features were specified against what it already did.
AI SDLC: questions people ask
What is the AI SDLC?
The AI software development lifecycle is a way of building software in which coding agents generate the code from written intent, and people decide what should exist, write the acceptance criteria and verify what was built.
How is the AI SDLC different from adding Copilot or Cursor?
An assistant speeds up typing inside the old lifecycle. The AI SDLC changes the order of work: intent and acceptance criteria first, generation second, verification as the developer’s main job. Without the first step, faster typing produces more unintended change.
How do we adopt the AI SDLC in a team?
Start with one or two real features, not a process rollout. Write the acceptance criteria before any code, let the developer build with their agent from them, and review against the criteria. The Feature1 POC does exactly this with your PM and one developer in one to two weeks.
Does this follow Anthropic’s AI-native SDLC playbook?
It follows the same shape: intent written first, agents build, people verify. Feature1 supplies the step the playbook starts after, which is where the intent comes from and how it stays current as the product changes.
Do we need to change our tools?
No. GitHub, GitLab or Bitbucket stay. Claude Code, Codex or Cursor stay. Feature1 adds the layer above them that holds the strategy, roadmap and acceptance criteria, and exposes it over MCP.
What is in the POC?
One to two weeks with your PM and one developer. We set the workflow up with your team and stay with you until two features are delivered from acceptance criteria held in Feature1. An NDA is signed before anything is read, GitHub access is read-only, and you pay the $500 only when the two features ship.
Is there a free trial?
Yes. Feature1 has a 7-day free trial with no credit card. Invite your team, write your first specs together and connect your coding agent.