/ SOLUTIONS
AGENTIC AI-DRIVEN SDLC
Our teams’ expertise, amplified by agentic AI
Agentic AI-driven SDLC is the operating model behind our redesigned project development lifecycle. At every phase, from requirements to security checks, a dedicated multi-agent system does the work and a senior professional supervises and validates it before it moves on to the next phase. We adopted it first on our own projects, in regulated and business-critical environments. Today we offer it to our Clients as a solution their teams can adopt.
[Human-led] engineering excellence, combined with [AI-driven] automation
01
A multi-agent system per phase
Seven phases, seven dedicated multi-agent systems. Each system works with precise instructions, project context and clear exit criteria for its phase. The output of each phase reaches the next one already structured and verifiable.
02
Human-led governance
Every phase has a senior owner (Project Manager, UX, Tech Lead, Software Architect, QA Engineer, DevSecOps) who validates the agent team’s work before it moves forward and remains accountable for it.
03
Enterprise stack
Our reference stack combines AWS Bedrock for model access with data residency constraints, Claude Code and GitHub Spec Kit for spec-driven development, Atlassian Rovo for governed integration with Jira and Confluence, and NotebookLM Enterprise for analysis. But the model doesn’t depend on the tools: we adapt it to the platforms and policies the Client already has in place.
04
Quality, security and costs under control
Code review, automated testing and DevSecOps checks are built into the workflow, within our ISO 27001-certified management system. AI usage costs are tracked by phase and by project.
Certifications & Partners
vegan/s operates a management system certified to the ISO 9001 and ISO 27001 standards
ISO 9001:2015 Certified
Certificate Number: 025D-VEGS-Q
ISO/IEC 27001:2013 Certified
Certificate Number: 025D-VEGS-SI
ISO 26000 Compliant
Frequently Asked Questions
How do you keep AI costs under control?
Cost monitoring is part of governance, just like technical oversight. We measure model usage by phase and by project, so every investment in automation can be weighed against the time and quality it delivers.
Who is accountable for the code produced by AI agents?
The engineering teams. Every phase transition requires validation by a senior professional, who is accountable for it just as they would be for a colleague’s work. It’s the same principle we apply to our own projects, where responsibility for every decision stays with whoever signs off on it.
Can we adopt the Agentic AI-driven SDLC model within our development team?
Yes, that’s exactly why we offer it as a solution. We start with an assessment of your existing development lifecycle, configure agents and quality gates on the tools you already use, and support your team through the first iterations. Adoption can be gradual: you start with the phases where the benefit is most immediate, then extend it to the rest of the lifecycle.
Is Agentic AI-driven SDLC only for the banking sector?
No. The model was developed on our own projects, many of them in regulated environments, but it’s industry-agnostic: it applies to any team that develops software and wants to move faster while maintaining high standards of quality and security.
