Agents All the Way Down; A Methodology for Building Custom AI Agents from Substrate to Production

2026-06-10 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, quick

Summary

The "Agents All the Way Down" methodology provides a comprehensive, framework-free approach for building custom AI agents from substrate to production. These agents are designed for specific applications, interacting with their own data and tools while enforcing security and maintaining audit trails. The methodology outlines two preconditions: (P1) Substrate, treating the LLM as a software component with tools, system, and prompt-caching messages; and (P2) Building blocks, including function calling, the Model Context Protocol (MCP), CLI orchestration, the liteshell pattern, and agent loops. It then details three repeated practices: (P3) prototyping with a general-purpose agent, (P4) harvesting and shipping the result as a CLI using the Turtle pattern, and (P5) agent-tests-agent for behavioral scenario testing. This practice was distilled from the AAC, a custom agent for the open-source LAMB platform, built in about ten days by one developer with an AI pair-programmer.

Key takeaway

For AI engineers tasked with developing specialized AI agents, adopting the "Agents All the Way Down" methodology offers a structured, framework-free path from concept to production. You should implement the two preconditions—Substrate and Building blocks—and then iterate through the three practices: prototype, harvest/ship as CLI, and agent-tests-agent. This approach streamlines development and testing, enabling rapid deployment of fit-for-purpose, maintainable AI solutions that integrate seamlessly into existing application ecosystems.

Key insights

This methodology offers a framework-free, end-to-end practice for building custom, fit-for-purpose AI agents from concept to production.

Principles

• Custom agents prioritize fit over general capability.
• Multi-agent orchestration is CLI composition.
• Agent-tests-agent complements classical testing.

Method

The "Agents All the Way Down" methodology involves two preconditions (Substrate, Building blocks) followed by a loop of three practices: prototype with a general-purpose agent, harvest/ship as CLI (Turtle pattern), and agent-tests-agent for behavioral scenarios.

In practice

• Utilize function calling and MCP for agent components.
• Implement the liteshell pattern for agent loops.
• Ship agent results as a CLI using the Turtle pattern.

Topics

• Custom AI Agents
• Agent Development
• Model Context Protocol
• CLI Orchestration
• Agent Testing
• AI Pair Programming

Best for: AI Architect, AI Scientist, Research Scientist, AI Engineer, Machine Learning Engineer, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.