Let's build a Coding Agent Harness from Scratch (Step by Step, No frameworks)

Item: Let's build a Coding Agent Harness from Scratch (Step by Step, No frameworks)
Rating: 5
Author: PostSingularity Institute

by Neural Breakdown with AVB · View the channel on PSI

Applied Sciences & Engineering AI — News & Applications UYComputer science UYQArtificial intelligence (AI)

🎙 Neural Breakdown with AVB 👥 35K 📅 September 8, 2026 ⏱ 48 min 👁 9K 📄 tutorial 🧭 2026-09-10

Available in: English (current) Français

Keywords

coding agentharnesscontext engineeringtool callingagentic loops

Summary

In this tutorial, the creator builds a coding agent harness from scratch without using any frameworks or shortcuts. The video is structured in multiple steps, starting with a minimal chat application using the DeepSeek-V4 Flash model via OpenRouter. Following that, a bash tool is introduced to execute shell commands, and file reading and writing tools are added. The core agent loop is implemented, where tool results are fed back to the model for iterative reasoning. The creator emphasizes context engineering, discussing prefix caching to reduce costs and the importance of not breaking the cache. Skills are introduced as markdown files that the agent can read when needed, and late injection is used to provide current information like date and git status without affecting the message history. To-dos help the agent manage long tasks. Advanced sections cover sandboxes and permissions, compaction, context capping, and subagents. The video demonstrates the agent solving a Wordle puzzle using a browser tool, sponsored by Egoite. Finally, a Boids simulation is generated by the agent. All code is available in the GitHub repository.

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Critical Evaluation

Value of the Information & Strength of the Argument

The video provides high value by offering a practical, step-by-step guide to building a coding agent. The argumentation is clear, as each design decision is explained with reasons, such as avoiding breaking prefix cache to reduce costs. The creator explains the purpose of each tool and how they work together. The live coding demonstrations reinforce the explanations and make the learning effective.

Scientific Rigor, Source Quality, Title Accuracy

The scientific rigor is apparent in the systematic approach and detailed code walkthroughs. The sources are the GitHub repository and the Egoite browser tool, which are directly used and referenced. The title accurately reflects the content, as the video indeed builds a coding agent harness from scratch. The content is well-structured with chapters, and the explanations are precise and factual.

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Title / Content Match

The title accurately describes the content: a step-by-step build of a coding agent harness from scratch.

Quality & Reliability

8/10

The video provides a thorough, technical walkthrough with working code. Explanations are clear and correct, with practical demonstrations.

Chapters

Cited Sources

neural-code GitHub repository — Source code of the harness built in the video.
Egoite Browser — Browser automation tool sponsored and demonstrated in the video.

Concurring Sources

neural-code GitHub repository — The source code aligns with the techniques discussed in the video.

Contribution & Novelties

The video contributes a clear, minimal implementation of a coding agent harness, emphasizing context engineering. It demonstrates practical techniques like prefix caching, tool design, and late injection.

Pour aller plus loin :

neural-code GitHub repo — Reference implementation.
Egoite Browser — Browser automation tool.
Advanced prompt engineering for LLM agents — Concept of context engineering (no URL).

57 words

Radar Profile

The profile shows high quality and quantity of information with a strong technical level, indicating a comprehensive and well-executed tutorial.

Reliability 8/10