How Fortified Health Security Rebuilt Its SDLC Around AI

Our Client

Fortified Health Security is a top-ranked MSSP specializing in cybersecurity for US hospitals and health systems.

Fortified Health Security is a top-ranked MSSP specializing in healthcare cybersecurity. They needed to accelerate software delivery without compromising the strict security, compliance, and quality guardrails that the domain demands. Their traditional SDLC relied on manual execution across requirements, coding, testing, and documentation, which made it difficult to keep pace with evolving regulations and a market moving faster than their delivery model could match.

Their flagship initiative, Central Command, set out to consolidate cybersecurity operations, enhance threat visibility, and improve client engagement. It required a development approach capable of integrating AI-driven capabilities while maintaining strict healthcare security and governance standards.

How did Fortified approach the change?

Fortified implemented an Agentic SDLC framework built on two pillars: context engineering and Spec-Driven Development. Fortified Health Security went further than approving the initiative, they championed it. Craig Badcock (VP Fortified Health Security) with SoftServe leadership drove both the organizational and workflow changes at every touchpoint, and the shared willingness to embrace and challenge the unknowns on a daily basis defined a strong partnership.

Leadership required the entire 20+ person team to adopt the agentic approach as the default way of working, not an optional experiment. Every new team member is onboarded directly into the framework, making agentic delivery the baseline rather than the exception. This level of client sponsorship has been the single most important accelerator.

The full delivery team (backend, frontend, QC, business analysis, and UI/UX) embraced an experimental mindset. Engineers actively test different AI agents, prompt strategies, and workflow configurations, sharing what works and discarding what doesn’t. The team built custom tools, including a RAG-for-Code solution (a retrieval technique that pulls the most relevant parts of the codebase into each AI prompt), to improve the relevance and accuracy of AI-generated outputs within the codebase. Structured training and onboarding programs turned engineers into effective AI operators, while shared context (project rules, templates, and standards embedded directly into AI workflows) ensures consistency across all streams.

At the core of the framework: Agent memory and instructions files are living, version-controlled documents containing architecture overviews, development standards, and AI agent guidelines. They act as a shared context layer for both engineers and AI agents, backed by a Research-Plan-Execute workflow that embeds governance checkpoints before any code is generated. What makes this approach distinctive is the architecture behind it: a single coordination repository acts as the authoritative source for all agent instructions and project knowledge. It aggregates, across multiple repositories, architectural maps, inter-repo dependency graphs, API contracts, business rules, compliance requirements, and domain glossaries into one unified knowledge base. This gives AI agents the same deep contextual awareness a senior engineer builds over months on a project.

Equally critical is how that context reaches the agent: rather than flooding it upfront, the framework applies progressive disclosure, staging and revealing context in layers as the agent advances through each phase of the task. The right architectural context surfaces during research, the relevant interface contracts appear during planning, and implementation standards govern execution, ensuring the agent always operates with precision rather than noise.

This redefined team roles across the board: Business Analysts moved from manually authoring requirements and specs to directing AI agents to generate user stories and documentation; Designers moved from hand-crafting prototypes to orchestrating AI-powered tools that produce UI concepts and production-ready assets; Engineers moved from writing every line of code to authoring system-level instructions; and QA Engineers transitioned from manual test creation to orchestrating AI-generated test suites.