Post by Galileo

As we've worked with countless AI teams to build, test and deploy agents, we've noticed an interesting trend: 𝐃𝐚𝐭𝐚 𝐬𝐜𝐢𝐞𝐧𝐜𝐞 𝐚𝐧𝐝 𝐬𝐨𝐟𝐭𝐰𝐚𝐫𝐞 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐚𝐫𝐞 𝐦𝐞𝐫𝐠𝐢𝐧𝐠. LLM-based features are built as software and tested like ML systems. There's a new workflow required to create and maintain the high-accuracy evals needed to test and govern these systems at scale. We're calling it eval engineering. Most teams can't trust their agents because they can't trust their evals. Industry research shows LLM-as-a-judge evals achieve only 70% accuracy, at best. Eval engineering solves this through a disciplined lifecycle: 1️⃣ Start with an LLM-as-a-judge prototype 2️⃣ Tune the eval with domain expertise to achieve 95% accuracy 3️⃣ Fine-tune a small language model eval for production-scale observability  4️⃣ Deploy as real-time guardrails that adapt to drift, and stop failures before they reach users The results speak for themselves: → A Fortune 50 telecommunications company scaled from 1 agent to 47 agents in 8 months → A Fortune 50 CPG business went from POC to production 15x faster and was able to observe 100% of their traffic Watch our CEO Vikram Chatterji explain the three core principles of eval engineering and why 2026 will be the year of trustworthy agents 👇 Plus, if you want to learn how to practice eval engineering yourself, we're launching a free 5-part course covering everything from eval creation to production guardrails. Register for the lessons below.

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