Smarter Together: Creating Agentic Communities of Practice through Shared Experiential Learning

TL;DR

This paper introduces Spark, a shared agentic memory architecture that enhances AI coding agents with collective experiential learning, significantly improving code quality and helpfulness in software development tasks.

Contribution

Spark is a novel shared memory system enabling AI agents to collaboratively learn and improve, bridging the gap between human communities and AI development practices.

Findings

Spark improves code quality across different AI models.

A small model with Spark matches larger models in performance.

Spark achieves up to 98.2% helpfulness in recommendations.

Abstract

The transition from human-centric to agent-centric software development practices is disrupting existing knowledge sharing environments for software developers. Traditional peer-to-peer repositories and developer communities for shared technical knowledge and best practice have witnessed dramatic drops in participation in a short period of time. At the same time, agentic functional equivalents are yet to emerge leaving AI agents, which already generate a significant proportion of all new software code produced, without access to repositories of valuable shared learning. In this paper, we introduce Spark, a novel shared agentic memory architecture which is designed to emulate the collective intelligence and know-how of human developer communities. Spark enables AI coding agents to both contribute to and draw from a persistent and continuously evolving experiential memory. Agents operating in the same general problem space use the Spark shared memory as a repository of new knowledge to achieve collective continual learning. We evaluate Spark as a coach for AI coding agents performing software development tasks. We demonstrate that recommendations made by Spark improve the quality of code generated by generic code generation models at varying sizes and capability tiers. Boosted by Spark, a small open-weights model with 30 billion parameters was able to match the code quality afforded by a much larger state-of-the-art model. Separately, we measure the intrinsic quality of recommendations generated by Spark against a wide range of criteria inspired by software development best practice, and achieve helpfulness levels of up to 98.2% in the top two (out of five) qualitative helpfulness bands.