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AGENTS.md: Defining Repository Rules for AI Agents
A practical root-and-subtree instruction design that gives coding agents durable commands, boundaries, verification steps, and review expectations.
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Agent Handoffs: Where Multi-Step Automations Break
Treat each agent handoff as a versioned contract with provenance, receiver-side acceptance, controlled rejection and recovery.
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When a Human Must Stay in the Agent Loop
Place human review at the consequence boundary, give approvers useful evidence and reduce oversight only when results support it.
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How to Measure an AI Agent Without Vanity Metrics
Build an agent scorecard around accepted outcomes, correction debt, exceptions and consequences instead of activity volume.
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A Practical Permission Model for AI Agents
A capability-based model for agent access that separates observation, proposal, approved execution, bounded execution, and denial.
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AI Agent Collaboration: Roles, Messages, and Shared Context
A practical architecture for multiple agents that assigns completion ownership, constrains specialist roles, and makes every handoff and conflict reviewable.
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Why Agent Memory Must Not Be Agent Authority
Memory, policy, and execution need separate owners, evidence, and lifecycles so stale context cannot become permission.
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The Acceptance Test Every Business Agent Needs
A practical acceptance-test design for business agents, using state transitions, adversarial fixtures, independent evidence, and safe recovery.
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Put AI Agent Metrics Under an Evidence Gate
AI4SALE implements evidence controls for one decision-critical AI-agent claim. The visible service traces events, formulas, permissions, evaluation cases, release criteria, rollback conditions,…
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