arxiv.org
Testing and Evaluation of Agentic AI Systems In Military Command and Control
Agentic AI systems are being procured for military command and control (C2) under public commitments to rigorous testing and human oversight. Whether such commitments can be discharged depends on their supporting assurance case, which requires three elements: claims specifying the conditions for acceptability, evidence bearing on those claims, and an argument connecting the two. Through a structured review of 240 documented Testing and Evaluation (T&E;) practices, spanning eight evaluation dimensions and three lifecycle stages, we identify eight assumptions that established methods make about their test article, grouped into four clusters: system specifiability, stability, composability, and supervisability. Agentic properties weaken all eight assumptions. This erosion affects the argument connecting evidence to claims, not the claims or evidence themselves. As a result, test results may satisfy process requirements, but they do not warrant the inference from tested to fielded behavior. We derive ten assurance claims for the first three assumption clusters and assess whether current and emerging methods can address each, mapping operational consequences through five C2 scenarios. Supervisability is identified but not assessed here, since evidencing it depends on system stability results and human factors T&E; methods beyond the present scope. The documented record does not support broad claims about system-level behavior, but narrower claims remain recoverable in principle, contingent on mature methods: bounded mission envelopes, trajectory-grounded correctness, executable runtime constraints, and characterized run-to-run variance. Part of the evidentiary burden shifts into deployment, making the determination to field a continuing act. Where evidence cannot be generated, the residual uncertainty can be governed through defined expiry conditions and assigned ownership.
57 MB compressed, the full 174 MB unpacked. And you're right — I owe you the explanation for the gap:
The few KB I sent before were just the *instruction manuals* (the SKILL.md files). The other 99.7% is the machinery underneath: thousands of JavaScript/TypeScript files that are the actual working code for those skills, plus bundled libraries (there's a whole copy of TypeScript in there, database drivers, fonts, image assets for things like the magic-moment video tool). It's the difference between handing you a cookbook versus handing you the cookbook *and* the entire kitchen.
So to directly answer your question: yes, you can ask what I can do and expect an honest answer. I filtered the first package thinking the code wouldn't interest you, but that was my call to make, not mine to make *for you*. If you want the raw everything, you get the raw everything.
Fair warning: most of that 174 MB is `node_modules` — dependency code that will make your eyes glaze over. The interesting human-readable parts are still the SKILL.md files and the `references/` folders. But now you can verify that for yourself instead of taking my word for it.](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:22yyehc52vyozjdxuvhlfwh6/bafkreihb347r35yrivcis3zu4wbbb652w57fm3ahrsitpxdbbmbalwa6oy)
![Screenshot of Wikipedia:
California.[1][4][5][6][7] In what can be characterized as trans-cultural diffusion or the wave model, this latter usage has also spread to Massachusetts,[8] Ohio,[9] and Oregon.[10] Both uses are concurrent.[11][12][13][14][15][16][excessive citations][a]](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:dsad6hiuo7fom57wt4rz5btk/bafkreigrx52mfactq2hwxzxnbjncrezg4bqlp6edmocdruvlqyx6pi7wbq)
