Loading...
Strategic Guide
How to make tool-connected agents safe enough for real permissions and real work.
Security frameworks and operational guardrails for MCP-connected 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 hard questions around failure mode and effects analysis for ai that expose blind spots early and force the system to prove it can survive scrutiny from more than one stakeholder group.
The governance model behind failure mode and effects analysis for ai, including ownership, override paths, review cadence, and the consequences that make governance real.
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 recurring breakdown patterns in public-sector automation and the Agent Trust controls that reduce avoidable risk.
How incident review should work for fmea for ai systems so teams can turn failures into reusable control improvements instead of expensive storytelling exercises.
How incident review should work for failure mode and effects analysis for ai so teams can turn failures into reusable control improvements instead of expensive storytelling exercises.
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.
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.
A first-deployment checklist for failure mode and effects analysis for ai that helps teams launch with clear boundaries, real evidence, and fewer self-inflicted trust failures.
The myths around failure mode and effects analysis for ai that keep teams from designing sound controls, setting fair expectations, and explaining the category honestly.
AI Agent Supply Chain Security matters because security risk in agent systems is increasingly shaped by prompts, tools, skills, dependencies, and runtime privileges, not just model APIs. This complete guide explains the model, the failure modes, the implementation path, and what changes when teams adopt it seriously.
A market map for failure mode and effects analysis for ai, focused on category structure, adjacent tooling, missing layers, and why the space keeps confusing different control problems.
The recurring breakdown patterns in legal automation and the Agent Trust controls that reduce avoidable risk.
Runtime enforcement is the discipline of making behavioral contracts matter after deployment by converting pact terms into gating, routing, escalation, and payment logic during live operation. This guide explains what it is, why serious teams care, and how Armalo turns it into a usable trust surface.
How operators should run ai agent trust in production without creating trust debt, brittle approvals, or hidden escalation risk.
A blueprint for an Agent Trust Operations Center that brings together monitoring, evaluation, risk review, and escalation for production agent fleets.
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.