TLDR Tech / Data
The hidden technical debt of agentic engineering
Agentic engineering creates the same kind of hidden technical debt ML teams faced ten years ago, but at higher speed: agents are easy to build locally, then quickly become hard to run safely at production scale. The core burden sits outside the agent itself: centralized integrations, live runtime context, decision traces, an agent registry, observability, evals, feedback loops, human-in-the-loop controls, governance, and orchestration. Platform teams need visibility and standardized controls early, or they'll retrofit them after incidents, cost overruns, or data exposure.
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