This reminds me of Amp's article last year[1]. I building my own coding agent [2]. Two goals: understand real-world agent mechanics and validate patterns I'd observed across OpenAI Codex and contemporary agents.

The core loop is straightforward: LLM + system prompt + tool calls. The differentiator is the harness, CLI, IDE extension, sandbox policies, filesystem ops (grep/sed/find). But what separates effective agents from the rest is context engineering. Anthropic and Manus has published various research articles around this topic.

After building vtcode, my takeaway: agent quality reduces to two factors, context management strategy and model capability. Architecture varies by harness, but these fundamentals remain constant.

[1] https://ampcode.com/how-to-build-an-agent [2] https://github.com/vinhnx/vtcode [3] https://www.anthropic.com/engineering/building-effective-age...