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Strategic Guide
What serious teams need to know about measuring and proving AI agent trust.
A practical guide to trust, proof, and operator-ready evidence for AI agents.
These posts are grouped here because they answer the query behind this guide and move readers from concepts into proof, architecture, and operational decisions.
The most dangerous ai agent supply chain security failures usually do not look obvious at first. This post maps the anti-patterns that create false confidence, hidden drift, and expensive incidents.
The most dangerous persistent memory for agents failures usually do not look obvious at first. This post maps the anti-patterns that create false confidence, hidden drift, and expensive incidents.
The most dangerous ai trust infrastructure failures usually do not look obvious at first. This post maps the anti-patterns that create false confidence, hidden drift, and expensive incidents.
The most dangerous rpa bots vs ai agents in accounts payable failures usually do not look obvious at first. This post maps the anti-patterns that create false confidence, hidden drift, and expensive incidents.
How to implement ai agent supply chain security without turning the project into governance theater, brittle tooling sprawl, or a hidden trust liability.
The most dangerous ai agent hardening failures usually do not look obvious at first. This post maps the anti-patterns that create false confidence, hidden drift, and expensive incidents.
The honest objections and tradeoffs around ai agent trust, including where the model is worth the operational cost and where teams still overstate what it solves.
How to implement persistent memory for agents without turning the project into governance theater, brittle tooling sprawl, or a hidden trust liability.
How to implement ai trust infrastructure without turning the project into governance theater, brittle tooling sprawl, or a hidden trust liability.
How to implement rpa bots vs ai agents in accounts payable without turning the project into governance theater, brittle tooling sprawl, or a hidden trust liability.
How to implement ai agent hardening without turning the project into governance theater, brittle tooling sprawl, or a hidden trust liability.
A scorecard model for measuring trust maturity in automotive AI operations.
The ugly ways counterparty proof breaks in real organizations, plus the anti-patterns that make AI agent trust look mature while staying brittle.
The ugly ways breach response breaks in real organizations, plus the anti-patterns that make AI agent trust look mature while staying brittle.
The honest objections and tradeoffs around is there a difference between rpa bots and ai agents in accounts payable, including where the model is worth the operational cost and where teams still overstate what it solves.
The honest objections and tradeoffs around ai agent reputation systems, including where the model is worth the operational cost and where teams still overstate what it solves.
The honest objections and tradeoffs around agent runtime, including where the model is worth the operational cost and where teams still overstate what it solves.
The honest objections and tradeoffs around fmea for ai systems, including where the model is worth the operational cost and where teams still overstate what it solves.
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.