𝐀𝐠𝐞𝐧𝐭 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞: 𝐁𝐞𝐲𝐨𝐧𝐝 𝐀𝐜𝐜𝐮𝐫𝐚𝐜𝐲

𝐀𝐠𝐞𝐧𝐭 𝐒𝐞𝐧𝐬𝐞 𝐢𝐬 𝐚 𝐬𝐡𝐨𝐫𝐭 𝐩𝐨𝐝𝐜𝐚𝐬𝐭 𝐨𝐧 𝐦𝐚𝐤𝐢𝐧𝐠 𝐀𝐈 𝐬𝐢𝐦𝐩𝐥𝐞, 𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥, 𝐚𝐧𝐝 𝐠𝐫𝐨𝐮𝐧𝐝𝐞𝐝 An AI agent can be 95% accurate and still fail thebusiness. It can give the right answer and still take too long, use the wrong tool, cost too much, or fail to complete the task. Frank Chávez and I talk with Jay Migliaccio, TechnicalProduct Manager for IBM watsonx Orchestrate, about how enterprises shouldmeasure and improve agent performance. Jay Migliaccio has spent his career in product and salesroles across early-stage software companies, and is now building agentic solutions for enterprise customers. That builder's view shapes everything below. Themes of the episode: 🔹 Why accuracy alone hides agent failures 🔹 How to measure outcomes, quality, operations,and user experience together 🔹 How observability traces a failure to the model,instructions, tools, workflow, data, or integration — IBM watsonx Orchestrateshipped a full observability dashboard this year for exactly this 🔹 Why teams need measurable goals and aperformance baseline before launch Jay's diagnostic approach: start with the businessoutcome, trace the full execution, find the real failure point before touchingthe model or the prompt. The Agent Performance principle: 𝑴𝒆𝒂𝒔𝒖𝒓𝒆𝒕𝒉𝒆 𝒐𝒖𝒕𝒄𝒐𝒎𝒆→ 𝑻𝒓𝒂𝒄𝒆 𝒕𝒉𝒆 𝒆𝒙𝒆𝒄𝒖𝒕𝒊𝒐𝒏→ 𝑭𝒊𝒏𝒅 𝒕𝒉𝒆 𝒇𝒂𝒊𝒍𝒖𝒓𝒆𝒑𝒐𝒊𝒏𝒕 → 𝑰𝒎𝒑𝒓𝒐𝒗𝒆𝒂𝒈𝒂𝒊𝒏𝒔𝒕 𝒂 𝒃𝒂𝒔𝒆𝒍𝒊𝒏𝒆 Agent Sense web page and all episodes: 🔗agentsensepodcast.com #AgentSense #EnterpriseAI #AgentObservability ⚠️ Disclaimer: The views shared are based on our personalexperience and do not represent the views of IBM.