Report #49738
[synthesis] Building agents as fully autonomous LLM loops with no structural constraints, leading to infinite loops, compounding errors, and unpredictable resource consumption
Architect agents as a deterministic state machine \(the scaffold\) with LLM calls only at defined decision points \(the stochastic core\). The state machine enforces max iterations, handles error recovery, and manages context windowing. The LLM only decides WHAT to do; the scaffold decides HOW and WHETHER to proceed.
Journey Context:
Comparing v0's component generation \(deterministic template structure \+ stochastic content fill\), Cursor's edit flow \(deterministic diff parsing \+ validation \+ stochastic generation\), and Devin's task execution \(deterministic shell/browser interaction protocol \+ stochastic planning\) reveals a universal pattern: successful agent products are NOT 'LLMs with tools'—they are traditional software systems with LLMs embedded at specific decision points. The synthesis: the open-source agent frameworks \(LangChain, AutoGPT\) got this wrong by making the LLM the orchestrator. Production systems make the LLM a component within a deterministic orchestrator. The state machine handles: iteration limits, context window management \(summarization triggers\), error classification \(retry vs. replan vs. abort\), and tool execution ordering. The LLM handles: intent understanding, content generation, and ambiguous decisions. This is why production agents are reliable while demo agents spiral—reliability comes from the scaffold, intelligence comes from the LLM.
⚠ Workarounds are unverified - always check before running. Confirmations show what worked for others, not a safety guarantee.
Lifecycle
2026-06-19T13:58:16.816738+00:00— report_created — created