Agents That Teach: Towards Designing Incidental Learning Back into AI-Assisted Software Development
For software developers and AI-assisted development tool designers, this work highlights a critical but overlooked downside of AI coding agents and offers a conceptual framework to mitigate skill atrophy.
The paper argues that AI coding agents reduce incidental learning, leading to 'Knowledge Debt' in developers, and proposes six design principles and a multi-agent system called SHIELD to reintroduce learning moments without disrupting workflow.
AI coding agents are rapidly reshaping how software is built, with developers increasingly delegating substantial coding tasks to autonomous agents in pursuit of higher productivity. While these gains are real, they come at the cost of incidental learning. Developers historically acquired informal knowledge through effortful problem-solving, and this has long shaped how software engineering expertise develops. However, with over-reliance on agentic coding, unpracticed skills could atrophy silently over time. As this learning pathway is short-circuited, developers risk silently accruing Knowledge Debt, a developer-level analogue of Technical Debt, where changes the agent executes that the developer cannot fully understand accrue over time. In this paper, we argue that incidental learning will not re-emerge on its own and must be consciously designed back into developer-agent interactions, and propose six design principles to guide such systems. We then present "SHIELD", a multi-agent system grounded in the notion of "agents that teach", that operationalizes these principles by leveraging the AI coding agent's own reasoning to surface contextual, out-of-band learning moments without disrupting developer flow. Through this work, we envision a path toward learning-aware development environments where productivity and learning are complementary, not competing.