AI 2025 trends

TL;DR Start a Recursive Trust Benchmark pilot by proving the measurement path before comparing reviewers. Validate the source cases, keep the answer key outside the candidate environment, freeze the trial assignments, and retain responses without silently repairing them. Account for every planned trial, including invalid and missing results. The original sixty-case starter supports decision-case development,

TL;DR The Recursive Trust Benchmark is a proposed method for comparing whether different assurance designs detect incorrect proposals, prevent prohibited effects, and produce independently supportable completion evidence. It separates reviewer comparison, direct control testing, and end-to-end agent evaluation so that improvements in one are not misrepresented as improvements in another. Version 0.1 includes a downloadable

TL;DR AI agent disaster recovery must address the possibility that the model, memory, policy, evaluator, or evidence is unreliable even while the infrastructure remains healthy. Restoring a runtime and reconnecting its previous state can recreate the failure. Recovery must establish an independently defensible operating baseline before execution authority returns. Recover different kinds of state differently.

TL;DR Human oversight of AI agents is useful only when people can identify material errors, obtain evidence outside the agent’s account, and intervene before the consequences exceed the approved boundary. An approval record proves that someone made a decision. It does not establish that the reviewer had the knowledge, information, time, or authority required to

TL;DR Independent agent assurance on Azure Local requires separate answers to three questions: can the workload continue, can the agent still obtain or exercise authority, and can an independent mechanism verify what happened? A functioning local application, cached gateway configuration, or valid credential does not answer all three. Design the action boundary for connected operation,

TL;DR Independent agent assurance on VMware Cloud Foundation (VCF) 9.1.1 requires more than separate tenants, protected model endpoints, and healthy infrastructure. The agent must remain unable to administer the controls that authorize its actions, obtain the executor’s credentials, or rewrite the evidence used to accept the result. Map those requirements across VMware vSphere Kubernetes Service

TL;DR An AI agent authorization architecture should let the agent propose work without letting it define the conditions that make that work permissible. Separate the runtime, authorization and enforcement, execution credentials, evidence, and human control administration. Protect the policy inputs and deployment paths as carefully as the policy engine itself. A denied action can become

TL;DR The Agent Action Evidence Contract defines what a consequential AI action must record, which component is responsible for each fact, and what evidence is required before the workflow can call the action complete. It connects authenticated identity, delegated authority, approved intent, execution attempts, independently observed results, and recovery obligations. Version 0.1 is a proposed

TL;DR The Assurance Independence Model evaluates whether the mechanisms overseeing an AI agent can fail, be manipulated, or be overridden through the same dependencies as the agent itself. It examines six dimensions: model, provider, context, enforcement, evidence, and organizational independence. Version 0.1 is a proposed DTD assessment framework, not an established standard or validated certification

TL;DR LLM-as-a-Judge uses a large language model to evaluate another system’s output, proposed action, or recorded behavior against defined criteria. It can make review more scalable, expose inconsistencies, and help identify problems that rigid checks miss. Its usefulness does not make its verdict an independent source of truth. A favorable score means that a particular