8/18/2026 · 1 min read
ai-agentsproduction-aievaluationsystem-outcomesmonitoringsalesforce-engineering
This article highlights a critical flaw in evaluating production AI agents: relying on conversational metrics can mask failures in executing real-world system outcomes. It advocates for a shift towards measuring tangible actions and their impact on backend systems.
The article posits that the failure of most AI agent pilots to reach production is due to a "control gap," not a technology gap. It introduces "Guided Determinism" as the solution to provide the necessary reliability and scalability for AI agents in enterprise production environments.
This article spotlights Zac Taylor from Salesforce's Trusted Services team, detailing their work on Security Center, an Agentforce-powered tool designed to accelerate AI-driven security investigations and remediation workflows.
Y Combinator has open-sourced QM, a self-hosted, model-agnostic, multi-agent harness designed for teams, offering a flexible alternative to tools like Anthropic's Claude Tag or Agentforce. While lacking a native Salesforce connector, QM provides significant potential for automation and AI-driven assistance once integrated.