LLM-as-Code: Agentic Programming for Agent Harness
Every major LLM agent framework gives the LLM the role of orchestrator; the model decides what to do next, when to call tools, and when to stop. We argue that token explosion, control-flow hallucination, and unreliable completion are not implementation bugs but architectural consequences of assigning the deterministic work of looping, branching, and sequencing to a probabilistic system. A better prompt or a stronger model cannot guarantee the reliability of the LLM agent. We therefore propose Agentic Programming, in which the program governs all control flow, and the LLM is itself part of it, an adaptive component we call LLM-as-Code and invoke only where a task calls for reasoning or gen...
| Authors | Junjia Qi, Zichuan Fu, Jingtong Gao et al. |
|---|---|
| Primary category | cs.AI |
| Published | 14 June 2026 |
| Last updated at source | 22 June 2026 |
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