AI SummaryA practical guide to writing maintainable, debuggable LLM agent code by addressing unique failure modes like non-determinism, opaque tool calls, and prompt-logic coupling. Developers building Claude-based agents will benefit from these patterns.
Install
Copy this and paste it into Claude Code, Cursor, or any AI assistant:
I want to set up the "Clean Code for LLM Agents" agent in my project. Please run this command in my terminal: # Add AGENTS.md to your project root curl --retry 3 --retry-delay 2 --retry-all-errors -o AGENTS.md "https://raw.githubusercontent.com/LCKYN/LCKYN-KnowledgeBase/main/10-19 Intelligence_Modeling/11_LLM_Dev/11.24 Clean Code for LLM Agents.md" Then explain what the agent does and how to invoke it.
Description
LLM agent code has unique failure modes that standard clean code practices don't fully address: **non-deterministic outputs**, **opaque tool calls**, **prompt-logic coupling**, and **hidden state in memory**. This note captures patterns specific to building maintainable, debuggable agent systems.
Overview
LLM agent code has unique failure modes that standard clean code practices don't fully address: non-deterministic outputs, opaque tool calls, prompt-logic coupling, and hidden state in memory. This note captures patterns specific to building maintainable, debuggable agent systems. ---
2. Design Tools as Pure Functions
Each tool should be: • Deterministic given the same inputs (or explicitly documented as non-deterministic) • Narrowly scoped — one capability per tool • Independently testable without an LLM `python
Core Principles
| Principle | Agent-Specific Meaning | |---|---| | Explicit over implicit | System prompt logic, tool routing, and memory access should be visible, not buried in framework magic | | Fail loud | Agent failures (tool errors, parsing failures, loop termination) should surface immediately, not silently produce wrong answers | | Reproducibility first | Log inputs, outputs, tool calls, and intermediate reasoning so any run can be replayed | | Separate concerns | Prompt = intent; Tool = capability; Orchestrator = control flow; Memory = persistence | | Testability | Each component (prompt template, tool function, parser) is individually testable without the full agent | ---
1. Separate Prompt from Code
Anti-pattern: embedding prompt strings inside orchestration logic. `python
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