The Agent Is 20% of the Work. The Platform Is the Other 80%.
An enterprise AI agent deployed for payroll processing achieved 94% accuracy in testing but only 70% in production due to unanticipated real-world input variations. The team improved accuracy to 98% through shadow testing and infrastructure enhancements, not model changes. This highlights that successful AI deployment depends more on platform engineering than on the agent model itself.
- ▪The payroll AI agent processed over 3,000 emails daily, performing six-step data extraction and classification.
- ▪Test accuracy was 94%, but production accuracy dropped to 70% due to unanticipated inputs like typos, screenshots, and conflicting instructions.
- ▪Shadow testing over four weeks, where AI outputs were reviewed alongside human work, increased effective accuracy to 98% without changing the agent's model.
- ▪The agent engine represented only 20% of the work, while platform infrastructure like evaluation pipelines and input governance made up the remaining 80%.
- ▪Without proper evaluation infrastructure, teams risk outsourcing their AI learning to vendors who control their testing and feedback loops.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3861685) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Todd Linnertz Posted on May 17 • Originally published at devopsdiary.blog The Agent Is 20% of the Work. The Platform Is the Other 80%. #ai #platformengineering #devops #mlops Governing AI in the Enterprise (2 Part Series) 1 AI Doesn't Fix Your Development Problems. It Accelerates Them. 2 The Agent Is 20% of the Work. The Platform Is the Other 80%. Originally published at devopsdiary.blog. Post F-AID1 in the "Governing AI in the Enterprise" series.
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