Loop Engineering, Clearly Explained!
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Loop engineering is the discipline of designing the outer control cycle that wraps an LLM agent. While the core agent loop is trivial and nearly identical across frameworks like LangGraph and the OpenAI Agents SDK, the real engineering challenge lies in four surrounding layers: defining reliable completion conditions (not trusting the model's self-assessment), managing context rot and doom loops through compaction and offloading, designing focused non-overlapping tools with idempotent writes and actionable error messages, and placing an independent verifier in the loop so 'done' is proven rather than claimed. A practical build-up sequence is provided: add hard caps first, define automated success criteria upfront, protect context, audit tools, and only go fully autonomous once a trusted critic is in place.
