White Paper v.1.3

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ABSTRACT

This document outlines the research and development efforts behind djinni.ai's fully automated software development platform, which aims to transform traditional software creation through AI-powered automation. The project addresses the biggest technical hypothesis: whether a multi-agent system (MAS) using large language models (LLMs) can autonomously develop complex software systems through long-running processes while delivering high-quality output.

The R&D journey began with advanced prompting techniques, evolving into a proprietary MAS framework designed to handle the challenges of coding automation. Initial experiments identified error accumulation as a critical issue, which was mitigated by modeling software development as a series of conversations between AI agents. In June 2024, the first success was achieved with a system built entirely by the Coding Daemon, validating the feasibility of the approach.

Subsequent experiments tested various LLMs, with the claude-3-opus model achieving an 82% success rate in meeting software specifications. Metrics for correctness, efficiency, and cost-effectiveness were established, demonstrating up to a 30x reduction in deve- lopment costs and a 24x increase in efficiency compared to traditional methods. Long term djinni.ai is aiming for five nines (99.999%) success rates.

The R&D efforts continue to focus on scaling the platform's capabilities to handle more complex systems, codifying more nuanced technical knowledge in advanced heuristics, and optimizing infrastructure to achieve convergence times under three minutes. Future work will incorporate open-source LLMs, custom hardware, and proprietary models to improve efficiency and reduce costs further.