Businesses are increasingly integrating agentic AI, where systems act with autonomy, into their core operations. As these AI agents take on more decision-making responsibilities, companies face new demands to ensure alignment with compliance requirements and ethical standards. The conversation is shifting from only boosting AI-driven productivity to establishing reliable oversight and robust safeguards as organizations pursue greater automation. For many, low-code development platforms are surfacing as a practical way to implement these controls while allowing the business to scale AI initiatives securely.
Some reports over the past year highlighted the rising adoption of AI for streamlining tasks, primarily focusing on predictive analytics and automation in discrete business functions. New developments point toward a broader use of agentic AI capable of acting independently with minimal human input. Earlier discussions mostly centered on the technical capabilities of AI, while today’s debates increasingly address transparency, risk, and accountability as these systems become integral to decision-making and operations.
What Is Driving a Shift Toward Agentic AI?
The move to agentic AI reflects changing expectations for technology’s role in business, shifting from passive data analysis to proactive operational management. Companies now envision agents that can autonomously resolve customer queries or dynamically adjust business processes as situations evolve. This enhanced autonomy creates new possibilities, but it also amplifies the need for established controls. As AI becomes less predictable, oversight mechanisms must be embedded into technology ecosystems to maintain company standards and avoid conflicts with regulations.
How Can Safeguards and Transparency Be Maintained?
Traditional software development emphasized consistency and defined outcomes, but agentic AI systems require a different approach. Developers are being asked to set supervisory parameters and transparency measures instead of just writing code. OutSystems notes that the importance of visibility in decision-making is recognized by technology leaders, as highlighted in its recent study showing that “
64% of technology leaders cite governance, trust and safety as top concerns when deploying AI agents at scale
”. By focusing on transparency and embedding accountability, teams can keep autonomous agents aligned with organizational policies even when they respond differently to identical input.
Can Low-Code Platforms Offer a Solution?
Low-code platforms, such as those offered by OutSystems, provide organizations with tools to implement agentic AI while retaining oversight. By merging application and agent development, these platforms simplify governance, compliance, and security processes. OutSystems stresses, “
By embedding governance and compliance into development, organizations gain confidence that AI-driven processes will advance strategic goals without adding unnecessary risk.
” This approach equips companies to scale up AI deployments across existing enterprise systems without significant overhauls, ensuring that oversight mechanisms grow hand-in-hand with technological expansion.
While excitement around agentic AI continues to grow, practical concerns about risk management and secure deployment remain at the forefront. Past coverage paid less attention to the governance challenges now dominating the conversation as businesses move from experimental use to mainstream adoption of autonomous agents. Observers stress that organizations should frame AI adoption strategies not only around innovation and efficiency, but also around transparency, accountability, and resilience. Low-code environments offer a bridge—allowing for flexible AI scaling while keeping trust and control at the core, which many see as a necessary condition for responsible enterprise automation.
- Agentic AI’s rise has increased focus on governance and transparency in deployment.
- Low-code platforms, like OutSystems, help embed compliance into autonomous systems.
- Oversight mechanisms must evolve with the growing autonomy of enterprise AI agents.
