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2026

Principles do not field themselves: why I am writing RAIDops

I did not set out to write a book.

RAIDops began during my Master of Science studies in Columbia University's Information & Knowledge Strategy program. An independent study, guided by Blake M. DiCosola III and strengthened by the advice and input of Edward J. Hoffman, started as a literature review of trustworthy artificial intelligence in national defense. Blake and Ed are both IKNS faculty; Ed previously served as NASA's first Chief Knowledge Officer.

The review grew into a long paper. The paper kept returning to an unresolved organizational problem. Eventually, the problem outgrew the paper.

I invented RAIDops and coined the name Responsible AI Development Operations for the framework that emerged from that work. The working monograph is coauthored with Blake and Ed, whose substantive intellectual contributions, guidance, editing, and mentorship have materially shaped it.1 Their collaboration has made the work substantially better.

I have hesitated to write publicly about RAIDops because the research program is active and the manuscript is unfinished. I am not going to reproduce the complete pattern catalog, assessment instruments, or the book's full analytical machinery here. But a framework concerned with reviewability should itself be open to review. This essay offers the public argument: enough to explain and defend RAIDops, while preserving the monograph as the place where the complete derivation, architecture, patterns, evidence controls, and limitations belong.

The thesis is straightforward: trustworthy AI is not merely a property to test in a model. It is an operating achievement that an organization must repeatedly produce, challenge, bound, preserve, and sometimes revoke.

That problem is not abstract to me. It recurs across my work in enterprise AI engineering, knowledge systems, and public-sector technology: building a capable system is only part of the job. The institution must also keep the purpose, evidence, authority, and means of intervention intact as the system crosses teams, contracts, environments, and time.

Multi-agent systems need a shared world, not just shared messages

The proposal window for the Defense Advanced Research Projects Agency's (DARPA) Decentralized Artificial Intelligence through Controlled Emergence program closed yesterday. DICE asks a difficult systems question: can heterogeneous artificial intelligence agents coordinate through peer-to-peer interaction, adapt when individual agents fail or become compromised, and still remain aligned with commander's intent over long missions?

Two days earlier, researchers released a preprint describing an “ontology as a kernel” for language-model agents. The proposed system makes domain concepts, relationships, evidence, and permissible reasoning operations explicit instead of leaving all of them implicit in prompts and unstructured context.

Those developments come from different research communities, and neither proves the other's architecture. Together, they expose the same design boundary: a collection of agents does not become a system merely because the agents can exchange messages. It becomes a system when they can coordinate around a shared, inspectable, and governed model of the world and the work.

That is a semantic-systems problem.

A follower is not an audience

Earlier today I posted something deliberately blunt on LinkedIn. I said the feed increasingly felt less like a professional network and more like Facebook: crowded with clickbait, personal-brand theater, and content optimized to provoke a reaction rather than help someone find work, exchange expertise, or make a useful professional connection.

Three hours later, the post showed 42 impressions. My profile has roughly 16,500 followers. Two people had reacted. Above the post, LinkedIn displayed an invitation to Boost it.

I took a screenshot because the juxtaposition was almost too perfect. A platform had offered me a number suggesting extraordinarily weak organic distribution and, in the same interface, offered to sell me more visibility. Then I wrote a second post criticizing that result. That complaint reached about 250 impressions in its first hour—roughly six times the number shown on the original post.

The easiest conclusion is also the most emotionally satisfying: LinkedIn suppressed my post so it could charge me to reach the audience I had already built.

The screenshot does not prove that. It reveals something more defensible, and in some ways more consequential: a follower on a ranked platform is not an addressable audience. It is a relationship that the platform may or may not activate, according to objectives the user cannot see.

That distinction matters on any social network. It matters more on a platform that mediates employment, expertise, reputation, recruiting, sales, and access to professional opportunity.

The first deliverable from generative coding should be understanding

The most dangerous sentence in a modernization program may be, “We know what this system does.”

Usually, someone knows what the system is supposed to do. Operators know the screens and workarounds. A few engineers know where the brittle integrations live. Program managers know the contracts and milestones. Cybersecurity teams know some of the exposed surfaces. The source code knows all of it at once—but in a form no single person can hold in mind.

That is why the emerging market for generative coding matters to government for a reason deeper than writing software faster. Its first serious public-sector use may be helping an organization recover a working model of the technology estate it already owns.

The CMMC suspension is an organizational knowledge test

Cybersecurity certification was never going to be just another audit for the companies that build and support U.S. defense systems. Preparing for it changed budgets, architectures, hiring plans, subcontractor relationships, and the everyday work connecting cyber teams with engineers, contracts staff, and program leaders.

Then, on July 13, 2026, the Department of Defense (DoD) suspended the planned transition to Phase II of the Cybersecurity Maturity Model Certification (CMMC) program. The Department also halted later implementation milestones and opened a 60-day review of the program.

The announcement brought meaningful relief from the coming expansion of third-party assessments. It did not, however, abolish CMMC or erase the contractual duty to protect defense information. Phase I continues, and the underlying safeguarding and reporting requirements remain in force.

That leaves defense organizations with a harder question than whether to keep preparing for an assessment: Which parts of the CMMC effort were merely certification overhead, and which parts became necessary organizational capability?

The distinction matters because a compliance program leaves behind more than policies and evidence. It also changes who talks to whom, how work moves between teams, where decisions are recorded, and whether an organization can accurately explain the security of its systems. Those capabilities are slow to build and surprisingly easy to lose.

From signals to strategic options

Organizations rarely suffer from a shortage of information. They face a conversion problem.

Research, technical releases, policy changes, customer behavior, competitive moves, hiring patterns, and weak signals from adjacent markets arrive continuously. Most are read once, summarized once, and then lost. Even when information is saved, the connection between what changed, what it might mean, which option it informed, and what happened next is rarely preserved.

Agentic AI creates an opportunity to improve that conversion—not by automating strategy, but by helping people build a more continuous and disciplined intelligence practice.

Model the builders, not only the models

We usually evaluate high-stakes AI by inspecting the artifact: the model, benchmark, system card, test report, or approval package. But before deployment, accountability is created—or eroded—by a population of builders acting through partial information, uneven authority, deadlines, review queues, and AI-mediated tools.

That makes builder-side AI development a natural candidate for agent-based modeling.

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