Inception
Your intent becomes a clear, testable specification.
- Requirements and user stories
- Acceptance criteria
- Units of work, worked out with your stakeholders
The speed of AI with the discipline of engineering
We build software with the AI-Driven Development Lifecycle (AI-DLC). AI agents write specifications, code and tests. Our engineers make the decisions, review every change and own the result.
Why a new lifecycle?
Two-week sprints were designed for teams who type every line by hand. When AI can draft a feature in an hour, the slow part becomes deciding what to build and checking it is right.
AI-DLC, introduced by AWS in 2025, puts AI at the centre of the work and puts people at the decision points. Work moves in short cycles called bolts, measured in hours or days, and a person approves the output of every phase.
Our rule
AI accelerates the work. Engineers own the outcome. Nothing reaches your users without a person reviewing it.
Three phases, three approval gates
Each phase produces written artefacts you can read. Each gate is a decision made by a person.
You approve the spec
Construction
Design, code and tests in short bolts.
- Architecture and technical plan
- Test-first implementation by agents
- Engineer review on every pull request
Code review passed
Operations
Ship, observe and improve.
- CI/CD on Azure DevOps or GitHub Actions
- Deployment to Azure or AWS
- Monitoring and AI-assisted incident response
A release sign-off closes every cycle before users see the change.
Spec-driven, not prompt-driven
The specification is the source of truth. Plans, tasks and code are generated from it and checked against it, so results are predictable and every line can be traced to a requirement.
GitHub Spec Kit
A step-by-step workflow: constitution, specify, plan, tasks, implement. Each step writes a file the team reviews before the next one runs.
We use it for focused features and changes to existing products.
BMAD Method
AI agents play agile roles (analyst, product manager, architect, scrum master, developer, QA) and produce a PRD, architecture and story files.
We use it for larger new products that need full planning.
Superpowers
Skills for coding agents that enforce good habits: brainstorm, write a plan, build test-first, then request a code review.
We use it for disciplined, test-driven execution of each task.
The AI toolchain our engineers use
Every engineer is trained on these tools. On your project we use only the tools you approve.
Claude and Claude CodeLong, multi-file changes, refactoring, specs and code review
CursorAI-first editor for day-to-day spec-to-code work
GitHub CopilotIn-editor suggestions, agent mode and pull request review
Azure DevOpsBoards, repos, pipelines and test plans
GitHub ActionsCI/CD for repos hosted on GitHub
Automated quality gatesTests, coverage, static analysis and secret scanning on every build
Guardrails on every project
Speed is only useful if the software is right and your code is safe.
Human review, always
Every pull request is reviewed by an engineer before it is merged.
Tests before code
Acceptance criteria become automated tests, and builds fail if coverage drops.
Security checks
Static analysis, dependency checks and secret scanning in every pipeline.
Your code stays yours
No client code is used to train models. NDA and IP assignment on every engagement.
Documentation by default
Specs, plans and decisions are kept in the repo, so your team can maintain what we build.
Approved tools only
We agree the list of AI tools with you at kickoff and stick to it.
What changes compared with a traditional sprint
| Topic | Traditional agile | AI-DLC at Credible |
|---|---|---|
| Cycle length | Two-week sprints | Bolts of hours or days |
| Requirements | Tickets and conversations | An approved, versioned specification |
| Who writes code | Developers | AI agents, directed and reviewed by engineers |
| Tests | Often written after the code | Generated from acceptance criteria, before the code |
| Documentation | Written at the end, if time allows | Produced at every step and kept in the repo |
| Your visibility | Sprint demo every two weeks | Specs and pull requests you can review any day |
Questions about AI-DLC
Is code written by AI good enough for production?
Only after review. Agents work from an approved spec and tests, and an engineer reviews every pull request. The engineer is accountable for what is merged, exactly as on a traditional team.
Can you use AI-DLC on our existing codebase?
Yes. We start by documenting the current system as a spec, then add features or modernise it step by step. This works well for .NET Framework and AngularJS migrations.
Can our own team learn this way of working?
Yes. We run AI-DLC adoption programmes: training, playbooks and pairing with our engineers on a real project, using Spec Kit, BMAD, Cursor, Copilot or Claude.
What if our company restricts AI tools?
We agree the approved tools with you at kickoff, can work only inside your environment, and can use the model services you already license.
See AI-DLC working on your project
Start a project
Book a free 30-minute call with a solution architect. We'll suggest an approach, a team and a transparent estimate.
Book a free consultationHire engineers
Add forward deployed engineers or a dedicated team that works in your repo, your stand-ups and your time zone.
See engagement modelsPrefer to talk now? Call +91 99098 71091 or message us on WhatsApp.