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Rahul Agarwal
Founder | Agentic AI...ย โขย 5h
Shipping an AI agent demo is simple. Operating one safely is not. A recent paper from Google reveals where the real engineering effort actually goes. Roughly eighty percent of deployment work focuses on reliability, governance, and operational infrastructure. Not the model. Not clever prompts. Not the reasoning engine. The real challenge is everything around it. Many teams build a working prototype in days and spend months preparing it for production. Because failures rarely look like model errors. They look like business problems. A support agent accidentally approves free orders because guardrails were never enforced. Sensitive information becomes visible because authentication rules were loosely implemented. Monitoring is missing. Continuous evaluation was never built. This is what production systems expose. AI agents behave differently from traditional software architectures built around fixed execution paths. Agents dynamically assemble tools and actions depending on context and intermediate reasoning steps. That flexibility requires strict access control, versioning systems, and deep observability. State management becomes harder. Memory across conversations must stay consistent, secure, and scalable under heavy usage. Costs are unpredictable. Different reasoning routes produce different latency patterns and compute consumption. Budgets, rate limits, and monitoring become essential safeguards. One principle stands out. Evaluation must gate deployment. Every agent update needs structured testing before reaching real users and real workflows. And not just the final answer. Intermediate tool calls and decisions must be evaluated too. Most tutorials never cover this part. But this is where real AI systems succeed or fail.
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