Components of a Coding Agent: How LLMs Become Software Engineers

2026-04-18 · Source: LLM on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, short

Summary

Coding agents, exemplified by tools like Claude Code and Codex CLI, are structured systems that extend large language models (LLMs) beyond simple chatbots to perform complex software engineering tasks. These agents integrate an LLM as a decision-making core within an iterative control loop, enabling dynamic problem-solving. Key components include an agent loop that cycles through observation, inspection, action, tool execution, and state updates; a tool interface for interacting with external systems like file systems and terminals; repository context for awareness of codebase structure; and memory management for tracking short-term task state and long-term session history. An "agent harness" orchestrates these components, managing prompts, tool schemas, execution, and loop control, significantly influencing overall system performance.

Key takeaway

For AI Engineers building LLM-powered development tools, recognize that an LLM alone is insufficient for robust coding tasks. Your focus should be on designing a comprehensive system that wraps the LLM with an iterative control loop, external tools, repository context, and robust memory management. Prioritize developing a strong "agent harness" to orchestrate these components, as it significantly impacts the system's overall performance and reliability in real-world software engineering scenarios.

Key insights

Coding agents transform LLMs into dynamic, multi-step problem solvers through structured system design.

Principles

• LLMs are decision engines, not full agents.
• Iterative loops enable dynamic problem-solving.
• Tool integration is critical for execution.

Method

Coding agents operate via an observe-act loop: LLM proposes an action, harness validates, tool executes, environment provides feedback, and the loop repeats.

In practice

• Integrate file system access for code agents.
• Implement memory for multi-step tasks.
• Develop a robust agent harness.

Topics

• Coding Agents
• Large Language Models
• Agent Control Loop
• Tool Integration
• Repository Context

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

Related on AIssential

• Components of A Coding Agent — Agentic harnesses significantly enhance LLM coding capabilities by integrating tools, context, and memory management.
• How AI Coding Agents Actually Understand Your Codebase — AI coding agents use tools and an iterative loop to understand code, plan, and execute tasks, mimicking human engineers.
• Building AI with AI — LLMs should be used to build systems, not as systems, reinforcing code's critical role.
• Programming with LLM Agents in 2025 — LLM agents, like Open Hands, significantly amplify developer productivity by automating iterative coding and visualization tasks.
• A Visual Guide to LLM Agents — LLM Agents augment basic LLMs with memory, tools, and planning for autonomous, complex task execution.
• Helping people write code again — LLM-assisted coding empowers experienced individuals to resume programming by reducing time barriers.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by LLM on Medium.