The classic “build versus buy” calculus for Department of the Air Force (DAF) IT systems is breaking down in an era where Agentic AI can generate prototypes, test harnesses, and documentation in hours instead of months. For Business and Enterprise Systems (BES) and Space Force ground and C2 environments, the question is no longer simply, “Do we custom-build or purchase COTS?” It is increasingly, “How do we blend build, buy, and AI‑accelerated configuration so we can iterate at mission speed?”

This session will explore how Agentic AI fundamentally changes the economics, timelines, and risk profile of modernizing and refactoring legacy DAF systems—particularly where technical debt and poor documentation have historically pushed programs toward large, inflexible COTS solutions. We will examine how coordinated, role-specific AI agents enable a radically faster requirements loop by transforming mission narratives and CONOPS into structured user stories, generating multiple solution sketches for side‑by‑side evaluation, simulating workflows and edge cases with synthetic data, and continuously refining requirements using real-world feedback.

We will show how this capability reshapes the build-versus-buy conversation: “buy” is no longer the only low‑risk option when custom builds can be quickly vetted against rich AI-derived requirements and test suites; “build” no longer means starting from a blank page when agents can generate boilerplate code, integration scaffolding, and compliant documentation; and hybrid models—where core platforms are purchased but tailored and integrated with Agentic AI—become increasingly attractive.

Critically, the session will emphasize that Agentic AI does not eliminate the need for rigorous requirements discipline—if anything, it raises the bar. The speed of AI‑accelerated development amplifies the consequences of ambiguous requirements. We will discuss how agile itself evolves with Agentic AI: from static story writing to continuous requirements discovery, from vague acceptance criteria to executable requirements, and from ad hoc architecture decisions to architecture explicitly shaped by AI‑assisted requirements analysis. Through BES- and Space Force‑relevant scenarios, we will illustrate when AI‑enabled, lighter‑weight builds are preferable, and when AI analysis supports choosing mature commercial offerings.

Participants will leave with a practical framework to reassess build-versus-buy decisions in the presence of Agentic AI, implement AI‑augmented requirements pipelines that keep pace with mission change, and ensure that requirements quality improves—not erodes—as development accelerates.

Key Learning Objectives

By the end of this session, participants will be able to:

• Explain how Agentic AI changes the traditional build-versus-buy equation for BES and Space Force ground/C2 systems, including impacts on cost, schedule, and risk.

• Describe how AI agents can accelerate and enrich the requirements lifecycle by transforming mission narratives into structured requirements, generating solution options, and simulating workflows and edge cases.

• Evaluate when custom builds, COTS solutions, or hybrid approaches are most appropriate in an AI-enabled environment, using AI-derived evidence on effort, risk, and maintainability.

• Define how agile practices evolve with Agentic AI, including continuous requirements discovery, executable requirements, rapid optioneering, and architecture decisions driven by AI‑assisted analysis.

• Apply a practical decision and governance framework to reassess current and future modernization efforts, ensuring that rapid AI‑assisted development is matched by improved requirements quality, provenance, and accountability.