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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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Deploy Local AI With a Measured Production Boundary
AI4SALE benchmarks one local AI workload on representative inputs, sizes hardware, maps data movement and privacy controls, and tests operating limits. The…
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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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Automatic Bot Error Recovery: Where Self-Healing Is Safe
A governance pattern for letting a bot recover from bounded operational failures while keeping diagnosis, verification, rollback, and escalation observable.
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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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AI Agent vs Automation: Which One Does the Job Better
A workflow-level comparison of deterministic automation, bounded agents, hybrid design, independent checking, and safe exception handling.
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