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 policies, and observability routines are patched together faster than they can be validated, standar
As agentic AI systems move from research to production, the complexities of managing their evolving, adaptable nature necessitate new frameworks for understanding and governing their inherent liabilities.
For strategic readers, this signals the emergence of critical governance and operational challenges that will determine the reliability, safety, and scalability of future AI infrastructure.
Traditional software and predictable machine learning technical debt models are insufficient, requiring a new classification of 'Agentic Technical Debt' to manage the risks and liabilities of autonomous AI systems.
- · AI governance specialists
- · AI system architects
- · Observability and monitoring tool providers
- · Risk management professionals
- · Organizations ignoring AI technical debt
- · Legacy software development methodologies
- · Ungoverned AI product teams
Increased focus on robust governance frameworks, validation, and standardization for agentic AI deployments.
Development of specialized tools and roles within organizations to manage Agentic Technical Debt, potentially leading to new compliance requirements.
Impact on AI adoption rates in critical infrastructure due to perceived risks and the cost of managing this new class of technical debt.
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Read at arXiv cs.AI