Not Every Node in Your Agent Needs an LLM

2026-05-21 · Source: Towards AI - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, medium

Summary

The article advocates for a refined design pattern in LLM-based agent pipelines, challenging the common practice of employing large language models for every processing node. It argues that using LLMs for tasks that require deterministic answers, such as classification, retrieval ranking, or validation, introduces unnecessary fragility, latency, and debugging complexity. Instead, the proposed "Bounded Validate–Rewrite Loop" architecture differentiates between "thinking" tasks, which genuinely require LLM reasoning, and "checking" tasks, which are better handled by deterministic code. An example six-phase agent illustrates this: LLMs are used for ambiguous intent routing, code generation, and targeted repair, while deterministic code handles documentation retrieval, API validation, grammar checking, and final response assembly. This strategic placement of LLMs enhances system determinism, diagnosability, and the ability to fail gracefully, leading to more reliable and understandable agent behavior.

Key takeaway

For AI Engineers designing agent pipelines, critically evaluate each node's function. If a step requires a verifiable answer, implement it with deterministic code rather than an LLM call, even for tasks like classification or validation. This approach significantly enhances system reliability, debuggability, and performance, preventing fragile, slow, and opaque agent behavior. Prioritize deterministic checks to catch errors early and enable targeted repair, leading to more robust and maintainable production systems.

Key insights

Agent pipelines should use LLMs only for reasoning tasks, not for deterministic checks.

Principles

• Match the tool to the task: LLMs for ambiguity, code for certainty.
• Deterministic checks improve diagnosability and graceful failure.
• Validate LLM outputs mechanically before acting on them.

Method

The "Bounded Validate–Rewrite Loop" pattern involves free-form LLM generation, mechanical verification via code, and targeted LLM repair under structured feedback, with a retry cap.

In practice

• Use code for API validation and grammar checks.
• Implement schema validators for LLM-generated SQL or JSON.
• Cap repair loops to prevent infinite retries.

Topics

• LLM Agents
• Agent Architecture
• Deterministic Systems
• Validation Loops
• AI Debugging
• Production AI

Best for: AI Engineer, Machine Learning Engineer, AI Architect

Related on AIssential

• Deterministic AI at Work: The LLM Is the Most Unreliable Function in My Pipeline, So I Treat It… — Treat LLMs as unreliable functions within a deterministic code pipeline to achieve reliable, auditable system outputs.
• Your AI Agent Backend Will Break in Production — A layered testing pyramid for AI agents separates deterministic backend logic from non-deterministic model outputs.
• Online Evals Done Right: Runtime Scoring and Review Queues for Production LLM Systems — Effective online LLM evaluation prioritizes deterministic checks and structured escalation over early tooling adoption.
• Ralph Wiggum (and why Claude Code's implementation isn't it) with Geoffrey Huntley and Dexter Horthy — Effective LLM agent design requires deterministic context management and human oversight, not autonomous "in-loop" operation.
• A Deterministic Control Plane for LLM Coding Agents — Unmanaged LLM agent configurations pose security and consistency risks, necessitating a deterministic control plane.
• Keep Deterministic Work Deterministic — Deterministic tasks in LLM pipelines should be handled by code to prevent cascading failures and improve reliability.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.