arXiv:2605.10907v3 Announce Type: replace-cross Abstract: The dominant paradigm for AI agents is an "on-the-fly" loop in which agents synthesize plans and execute actions within seconds or minutes in response to user prompts. We argue that this paradigm short-circuits disciplined software engineering (SE) processes -- iterative design, rigorous testing, adversarial evaluation, staged deployment, and more -- that have delivered the (relatively) reliable and secure systems we use today. By focusing on rapid, real-time synthesis, are AI agents effectively delivering users improvised prototypes ra
The rapid expansion of AI agent capabilities and deployment highlights the immediate need for robust engineering practices to ensure reliability and security.
The current 'on-the-fly' AI agent paradigm risks widespread deployment of unreliable and insecure systems, undermining trust and limiting their ultimate utility.
This research advocates for a shift from improvised AI agent development to incorporating disciplined software engineering processes, impacting how agents are designed, tested, and deployed.
- · Software engineering consultancies
- · AI safety and ethics organizations
- · Enterprises deploying critical AI agents
- · Users of robust AI systems
- · Developers prioritizing speed over reliability
- · Early-stage AI agent platforms lacking robustness tools
- · Organizations with immature AI governance
- · Users impacted by unreliable AI agents
AI agent development will begin to incorporate more rigorous software engineering principles and tools.
Increased trust and adoption of AI agents in mission-critical applications as their reliability improves.
The emergence of new regulatory frameworks for AI agent development mirroring traditional software compliance standards.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI