Tagline: The AI-First mandate is real. So is the risk of mandating it without the discipline that makes it safe.

Abstract:
In January 2026, the Department of War issued its AI-First strategy. The institutional pressure on program offices to demonstrate AI adoption is now explicit and documented. The risk of mandating that adoption without understanding what it amplifies is not.

Agentic coding tools, deployed on contractor teams that lack the engineering discipline to use them safely, can produce tomorrow's legacy system in months rather than decades. The defining characteristic of legacy software is the absence of human understanding. When agents generate code faster than developers can absorb architectural complexity, dependencies multiply, abstractions drift, and unmaintainable code accumulates at a rate no existing PMO oversight process is calibrated to detect. A contractor team that was already struggling with technical debt before the agents switched on will produce more of the same problems, faster, and at higher volume.

The emerging contracting environment will compound this risk. The White House executive order directing agencies toward fixed-price and performance-based contracts will create stronger pressure to define deliverables, acceptance criteria, and contractor accountability up front. That shift can creates a software-specific danger: if maintainability, architectural coherence, meaningful test coverage, and code ownership are not part of the acceptance criteria, contractor incentives will naturally optimize around visible functionality. Every hour spent improving qualities the contract doesn’t measure will reduce margin. Program offices that define “done” as visible functionality risk buying a performance gap they won’t see until sustainment.

Agentic coding can give defense software programs a major advantage when skilled engineers apply it against well-structured codebases under clear engineering standards. The danger is pushing teams to adopt AI coding tools without requiring those safeguards. That approach will produce a lot of code quickly, while weak maintainability and poor architecture stay hidden under fixed-price delivery pressure until after the government has accepted the software.

Attendees will leave with a concrete framework for what contractor AI adoption maturity looks like, what contract language and acceptance criteria close the oversight gap, and the 8 questions every program integrating AI-enabled development should be able to answer before an architecture decision is locked.

Learning Objectives:
1. Understand what the DoD AI-First mandate directs, what it leaves unaddressed at the program office level, and where the resulting contractor accountability gap sits
2. Apply the agentic coding risk framework to assess whether a contractor's team discipline is proportionate to the output velocity their AI tooling is capable of producing
3. Identify the specific acceptance criteria gaps that fixed-price AI-enabled contracts create, and what contract language closes them before award rather than after delivery
4. Recognize the architectural failure mode created when cloud-dependent agentic coding tools are fielded on programs with denied, degraded, intermittent, and limited connectivity, tactical edge, or contested-environment requirements
5. Use the 'Engineering Standards for Agentic Software Development' framework to evaluate contractor AI adoption maturity during source selection, DCMA surveillance, and annual performance reviews

Target Audience:
Program managers, deputy PMs, contracting officers, and systems engineers at Navy and Air Force program offices managing software development contracts under the Software Acquisition Pathway. Directly relevant to PEO staff at NAVAIR, NAVSEA, PEO C4I, AFLCMC, and Space Systems Command who are responding to AI-First guidance and evaluating contractor AI integration claims.

Format:
50-minute talk with 10-minute Q&A. Opens with a live demonstration of agentic coding output on a real codebase to make the amplification dynamic concrete before the policy analysis begins.