Loading...
Strategic Guide
A buyer-ready procurement guide for serious AI agent systems.
How buyers should assess, compare, and validate AI agent platforms.
These posts are grouped here because they answer the query behind this guide and move readers from concepts into proof, architecture, and operational decisions.
Ten high-leverage questions automotive buyers should ask to separate demos from dependable systems.
The high-friction questions operators and buyers ask about ai agent trust, answered plainly enough to survive procurement, security review, and skeptical follow-up.
A scorecard model for measuring trust maturity in automotive AI operations.
What serious buyers should ask, verify, and refuse when evaluating counterparty proof in AI agent vendors, platforms, and marketplace listings.
What serious buyers should ask, verify, and refuse when evaluating breach response in AI agent vendors, platforms, and marketplace listings.
What serious buyers should ask, verify, and refuse when evaluating runtime enforcement in AI agent vendors, platforms, and marketplace listings.
What serious buyers should ask, verify, and refuse when evaluating measurable clauses in AI agent vendors, platforms, and marketplace listings.
Ten high-leverage questions agriculture buyers should ask to separate demos from dependable systems.
The high-friction questions operators and buyers ask about ai agent reputation systems, answered plainly enough to survive procurement, security review, and skeptical follow-up.
The high-friction questions operators and buyers ask about is there a difference between rpa bots and ai agents in accounts payable, answered plainly enough to survive procurement, security review, and skeptical follow-up.
The high-friction questions operators and buyers ask about agent runtime, answered plainly enough to survive procurement, security review, and skeptical follow-up.
The high-friction questions operators and buyers ask about fmea for ai systems, answered plainly enough to survive procurement, security review, and skeptical follow-up.
The high-friction questions operators and buyers ask about identity and reputation systems, answered plainly enough to survive procurement, security review, and skeptical follow-up.
A scorecard model for measuring trust maturity in agriculture AI operations.
The high-friction questions operators and buyers ask about failure mode and effects analysis for ai, answered plainly enough to survive procurement, security review, and skeptical follow-up.
The high-friction questions operators and buyers ask about reputation systems, answered plainly enough to survive procurement, security review, and skeptical follow-up.
The high-friction questions operators and buyers ask about persistent memory for ai, answered plainly enough to survive procurement, security review, and skeptical follow-up.
The high-friction questions operators and buyers ask about ai trust stack, answered plainly enough to survive procurement, security review, and skeptical follow-up.
Trust Algorithms
This paper argues that Reputation Half-Life deserves attention as a core trust primitive in the AI agent economy. We examine how fast old performance evidence should decay when agents, prompts, tools, or economic incentives change, define reputation half-life model as the governing mechanism, and show why strong historical scores continue to grant access long after the underlying behavior has changed. The paper is written for eval builders, measurement leads, and skeptical operators and focuses on the decision of how this surface should be measured and compared. Our evidence posture is trust-model analysis informed by update and drift patterns, with emphasis on benchmark-backed framing and metric design.