{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Machine Learning Tech Brief By HackerNoon","title":"A Six-Step Framework for Auditing Enterprise AI Agents","html":"<iframe width=\"100%\" height=\"180\" frameborder=\"no\" scrolling=\"no\" seamless src=\"https://share.transistor.fm/e/f9dc585f\"></iframe>","width":"100%","height":180,"duration":344,"description":"\n        This story was originally published on HackerNoon at: https://hackernoon.com/a-six-step-framework-for-auditing-enterprise-ai-agents.\nA six-step framework for finding, scoring, consolidating, and retiring enterprise AI agents based on cost, value, ownership, and governance risk.\nCheck more stories related to machine-learning at: https://hackernoon.com/c/machine-learning.\n            You can also check exclusive content about #enterprise-ai, #ai-governance, #ai-cost-optimization, #agentic-ai, #finops, #ai-strategy, #ai-agents, #ai-agent-sprawl,  and more.\nThis story was written by: @eshaanjain26. Learn more about this writer by checking @eshaanjain26's about page,\n            and for more stories, please visit hackernoon.com.\nEnterprises spun up AI agents fast, and now many run dozens that overlap, duplicate work, and each carries a token bill and a governance risk. This is the next shadow IT. I run cost and governance on large Salesforce programs, and here is a 6-step method to inventory your agents, score them, and retire the ones that cost more than they return.","thumbnail_url":"https://img.transistorcdn.com/KyA01h2FD2insgk-wX_xzV6vbJnTNl2BvPYVL-XaI9A/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9zaG93/LzQxMjcyLzE2ODM1/ODI0ODgtYXJ0d29y/ay5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}