Agentic AI is no longer a research concept or a demo-only capability. It is being introduced into production systems that must operate under real constraints: predictable latency, bounded cloud spend, operational reliability, security requirements, and long-term maintainability. Autonomous agents that can reason, plan, collaborate, and act across distributed architectures promise significant leverage, but they also introduce a new cost model that many engineering teams underestimate.
Early implementations often succeed functionally while failing operationally. Agents reason too frequently, collaborate without limits, and remain active long after decisions have been made. What starts as intelligent autonomy quickly turns into inflated inference costs, unpredictable system behavior, and architectures that are difficult to govern at scale.
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