Governing Technical Debt in Agentic AI Systems

📰 ArXiv cs.AI

Learn to govern technical debt in agentic AI systems to ensure efficient and scalable infrastructure

advanced Published 29 May 2026

Action Steps

Identify potential sources of technical debt in agentic AI systems using tools like prompt analysis and memory audits
Analyze orchestration graphs to detect inefficiencies and optimize workflow execution
Develop control mechanisms to mitigate technical debt accumulation
Implement monitoring and feedback loops to track technical debt and inform system updates
Apply governance frameworks to agentic AI systems to ensure accountability and transparency

Who Needs to Know This

AI engineers, researchers, and developers working on agentic AI systems can benefit from this knowledge to improve system maintainability and reduce technical debt

Key Insight

💡 Agentic Technical Debt can be managed through prompt analysis, memory audits, and governance frameworks

Key Takeaways

Learn to govern technical debt in agentic AI systems to ensure efficient and scalable infrastructure

Full Article

Title: Governing Technical Debt in Agentic AI Systems

Abstract:
arXiv:2605.29129v1 Announce Type: new Abstract: Agentic AI systems are increasingly being explored as production infrastructure: they reason over multiple steps, call tools, act through workflows, and adapt through memory and feedback. These systems create governance challenges that are not fully captured by traditional software or predictive ML technical debt. We define Agentic Technical Debt as the accumulated liability created when prompts, memory, tool schemas, orchestration graphs, control

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