ASE-26: a curriculum for agentic software engineering as a discipline

2026-05-31 · Source: Artificial Intelligence · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Intermediate, quick

Summary

ASE-26 introduces a comprehensive undergraduate curriculum for agentic software engineering, addressing the industry's shift towards directing AI agents rather than solely writing code. Empirical evidence shows this trend, with Anthropic's Economic Index reporting 79 per cent automation in Claude Code interactions and Handa et al. finding 75 per cent AI exposure for computer programmer tasks. Brynjolfsson et al. also noted a 13 per cent relative decline in employment for 22 to 25-year-olds in AI-exposed occupations. The curriculum, deposited on Zenodo under CC BY-ND 4.0, comprises 21 modules and frames agentic software engineering as a distinct discipline. It emphasizes concepts like the "evolutionary spiral" for co-evolution of intent and build, and outlines pedagogical approaches for evaluating work co-produced with agents. ASE-26 aims to provide graduates with the specific practitioner skills currently lacking in the industry, ensuring the discipline's relevance beyond today's model capabilities.

Key takeaway

For AI Engineers or Software Engineering educators developing future talent, this curriculum highlights a critical shift. You should integrate agentic software engineering principles, focusing on directing AI agents and evaluating co-produced work, into your programs. This ensures graduates possess the structured practitioner discipline needed to navigate an evolving landscape where AI exposure for programmer tasks is already around 75 per cent. Prioritize skills that transcend specific model versions.

Key insights

Structured curricula in agentic software engineering are essential to close the industry's skill gap in directing AI agents effectively.

Principles

• Software engineering increasingly directs agents.
• Practitioner discipline is key, not just better models.
• Curricula must outlast current model capabilities.

Topics

• Agentic Software Engineering
• AI Agents
• Software Engineering Education
• Workforce Transformation
• Curriculum Design
• AI Impact on Employment

Best for: Software Engineer, AI Engineer, AI Student

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