Methodology for Building Custom AI Agents from Development to Production
A paper published on arXiv outlines a structured methodology called 'Agents All the Way Down' for building custom AI agents from initial setup through production deployment. The methodology was derived from building a real agent called AAC for the open-source LAMB platform, completed in roughly ten days by a single developer using an AI pair-programmer. It aims to consolidate fragmented practices currently scattered across blogs, podcasts, and leaked system prompts into a coherent, framework-free guide.
The paper, submitted to arXiv by Marc Alier on June 10, 2026, addresses a gap in published software engineering practice: while the individual components of custom AI agents exist (function-calling APIs, the Model Context Protocol, code agents), no unified methodology for assembling them end to end has been formally documented. The proposed methodology consists of two preconditions — establishing the LLM as a software component (Substrate) and assembling core building blocks such as function calling, MCP, and agent loops — followed by three iterative practices: prototyping with a general-purpose agent, harvesting and shipping the result as a CLI via the 'Turtle pattern,' and using one agent to test another through behavioral scenarios. The authors argue that multi-agent orchestration reduces to CLI composition, simplifying a concept often treated as architecturally complex. The methodology is explicitly framework-free and language-agnostic, intended to be transferable across different development environments. Its practical grounding in a real production deployment lends it empirical credibility, though the case study involves only a single project built by one developer.
What's missing
The paper is based on a single case study (one agent, one developer, one platform), raising open questions about scalability to larger teams, more complex domains, or higher-stakes production environments. The methodology has not been independently validated or compared against existing agent-building frameworks in a controlled setting.
What different sources said
- arXiv cs.AICenter
Agents All the Way Down; A Methodology for Building Custom AI Agents from Substrate to Production
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