Post by Sairam Sundaresan

Building an AI agent is easy. Trusting it is hard. Google's latest whitepaper puts a number on it. 80% of the effort in deploying an AI agent goes to infrastructure, security, and validation. Not the model. Not the prompts. Not the intelligence. The operational discipline around it. Teams ship a demo in days, then spend months making it production-worthy. 🔸 The failure scenarios Google highlights: ↳ Customer service agent gives products away free (missing guardrails) ↳ Users access confidential data through the agent (improper auth) ↳ Weekend generates a massive cloud bill (no monitoring) ↳ Agent that worked yesterday suddenly fails (no continuous eval) These are business failures, not just technical ones. 🎯 Why agents are different from traditional software: 🔸 Dynamic Tool Orchestration ↳ Execution paths assembled on the fly ↳ Requires versioning, access control, and observability for each path 🔸 Scalable State Management ↳ Memory across interactions needs secure, consistent handling at scale 🔸 Unpredictable Cost and Latency ↳ Different reasoning paths make cost and response time hard to forecast ↳ Requires budgets, rate limits, and monitoring The core principle Google proposes: Evaluation-Gated Deployment. No agent version reaches users without passing quality and safety checks. Not just checking the final answer. Evaluating tool behavior and intermediate steps too. This is the part most agent tutorials skip entirely. Paper 👉 https://lnkd.in/gjfQd9CV ♻️ Repost to help someone avoid a costly deployment mistake ➕ Follow me, Sairam, for AI from lab to production ----- Join 26k+ readers from Google, Meta, Netflix, and over 160+ countries worldwide: https://lnkd.in/gZbZAeQW Learn the basics first: https://lnkd.in/gTQyc_fi