Building a Coding Agent From Scratch

2026-07-22 · Source: Decoding AI Magazine · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Cloud Computing & IT Infrastructure · Depth: Advanced, long

Summary

The "Building a Coding Agent From Scratch" open-source course, featuring the "Decode" agent, demonstrates that a coding agent's performance hinges on its harness, not solely the underlying LLM. A LangChain Terminal-Bench experiment showed a coding agent moving from ~30th to top 5 by changing only the harness. This 8-lesson Python course guides users through building a complete coding agent harness, starting from a basic agent loop to a swarm of remote agents. It covers essential modules like LLM providers (Modal, OpenRouter, Gemini), sandboxing (Docker, Modal), permissions, memory, skills, and an LSP server. The course also details interactive TUI and remote Kitaru-powered modes, alongside an observability and AI Evals layer using Opik for benchmarks and regression testing.

Key takeaway

For AI Engineers building custom agent solutions, this course highlights that investing in robust harness design is more impactful than solely optimizing LLMs. You should prioritize developing modular components for LLM providers, sandboxing, permissions, and context management. This approach enables building adaptable agents like "Decode" that can operate interactively or as scalable remote swarms, ensuring performance and security across diverse use cases.

Key insights

A coding agent's harness, not the LLM, is the primary driver of its performance and engineering value.

Principles

• Harness engineering is key to agent performance.
• Separate core logic from serving and infrastructure layers.
• Tight feedback loops accelerate agent convergence on working code.

Method

Build a coding agent in 3 phases: agent loop (Pydantic AI, Modal/OpenRouter), harness modules (Kitaru, context engineering, sandboxing, permissions, agent catalog), and AI Evals (custom benchmarks, Opik, GCP deployment).

In practice

• Implement sandboxing with Docker or Modal for secure tool execution.
• Use a steering queue to manage mid-task user input without corruption.
• Integrate an LSP server for immediate syntax and semantic feedback.

Topics

• Coding Agents
• Agent Harness Design
• AI Agent Architecture
• LLM Observability
• Sandbox Security
• Kitaru Runtime
• Context Engineering

Code references

• decodingai-magazine/building-a-coding-agent-from-scratch-course
• anomalyco/opencode
• earendil-works/pi
• aider-ai/aider
• langchain-ai/deepagents

Best for: AI Engineer, MLOps Engineer, AI Architect

Related on AIssential

• Continually improving our agent harness — Optimizing LLM agent performance requires a highly customized, iteratively refined, and dynamically managed harness.
• Agentic System Design Interview Series #1: Design a Coding Agent — Agentic system design requires building deterministic frameworks around non-deterministic LLM cores, emphasizing feedback loops and robust verification.
• [Hands-on] Rebuilding Claude Code's Harness — The agent harness, not just the LLM, defines advanced coding agent capability.
• Components of A Coding Agent — Agentic harnesses significantly enhance LLM coding capabilities by integrating tools, context, and memory management.
• The Anatomy of an Agent Harness — The agent harness, not the LLM, is the critical infrastructure enabling robust, multi-step AI agent performance.
• How to Make a Coding Agent a General Purpose Agent - Harrison Chase — Coding agents, with robust harnesses and secure tool runtimes, form the foundation for scalable, general-purpose enterprise AI agents.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Decoding AI Magazine.