Author: Rodrigo Coutinho, Co-Founder and AI Product Manager at OutSystems
AI has moved beyond pilot projects and future promises. Today, it’s embedded in industries, with more than three-quarters of organisations (78%) now using AI in at least one business function. The next leap, however, is agentic AI: systems that don’t just provide insights or automate narrow tasks but operate as autonomous agents, capable of adapting to changing inputs, connecting with other systems, and influencing business-critical decisions. Although these agents will deliver greater value, agentic AI also poses challenges.
Imagine agents that proactively resolve customer issues in real-time or adapt applications dynamically to meet shifting business priorities. The greater autonomy inevitably brings new risks. Without the right safeguards, AI agents may drift from their intended purpose or make choices that clash with business rules, regulations, or ethical standards. Navigating this new era requires stronger oversight, where human judgement, governance frameworks, and transparency are built-in from the start. The potential of agentic AI is vast but so are the obligations that come with deployment. Low-code platforms offer one path forward, serving as a control layer between autonomous agents and enterprise systems. By embedding governance and compliance into development, they give organisations the confidence that AI-driven processes will advance strategic goals without adding unnecessary risk.
Designing safeguards instead of code for agentic AI
Agentic AI marks a steep change in how people interact with software. It’s indicative of a fundamental shift in the relationship between people and software. Traditionally, developers have focused on building applications with clear requirements and predictable outputs. Now, instead of fragmented applications, teams will orchestrate entire ecosystems of agents that interact with people, systems and data.
As these systems mature, developers shift from writing code line by line to defining the safeguards that steer them. Because these agents adapt and may respond differently to the same input, transparency and accountability must be built in from the start. By embedding oversight and compliance into design, developers ensure AI-driven decisions stay reliable, explainable and aligned with business goals. The change demands that developers and IT leaders embrace a broader supervisor role, guiding both technological and organisational change over time.
Why transparency and control matter in agentic AI
Greater autonomy exposes organisations to additional vulnerabilities. According to a recent OutSystems study, 64% of technology leaders cite governance, trust and safety as top concerns when deploying AI agents at scale. Without strong safeguards, these risks extend beyond compliance gaps to include security breaches and reputational damage. Opacity in agentic systems makes it difficult for leaders to understand or validate decisions, eroding confidence internally and with customers, leading to concrete risks.
Left unchecked, autonomous agents can blur accountability, widen the attack surface and create inconsistency at scale. Without visibility into why an AI system acts, organisations risk losing accountability in critical workflows. At the same time, agents that interact in sensitive data and systems expand the attack surface for cyber threats, while un-monitored “agent sprawl” can create redundancy, fragmentation and inconsistent decisions. Together, these challenges underscore the need for strong governance frameworks that maintain trust and control as autonomy scales.
Scaling AI safely with low-code foundations
Crucially, adopting agentic AI need not involve rebuilding governance from the ground up. Organisations have multiple approaches available to them, including low-code platforms, which offer a reliable, scalable framework where security, compliance and governance are already part of the development fabric.
Across enterprises, IT teams are being asked to embed agents into operations without disrupting what already works. With the right frameworks, IT teams can deploy AI agents directly into enterprise-wide operations without disrupting current workflows or re-architecting core systems. Organisations have full control over how AI agents operate at every step, ultimately building trust to scale confidently in the enterprise.
Low-code places governance, security and scalability at the heart of AI adoption. By unifying app and agent development in a single environment, it is easier to embed compliance and oversight from the start. The ability to integrate seamlessly in enterprise systems, combined with built-in DevSecOps practices, ensures that vulnerabilities are addressed before deployment. And with out-of-the-box infrastructure, organisations can scale confidently without having to reinvent foundational elements of governance or security.
The approach lets organisations pilot and scale agentic AI while keeping compliance and security intact. Low-code makes it easier to deliver with speed and security, giving developers and IT leaders confidence to progress.
Smarter oversight for smarter systems
Ultimately, low-code provides a dependable route to scaling autonomous AI while preserving trust. By unifying app and agent development in one environment, low-code embeds compliance and oversight from the start. Seamless integration in systems and built-in DevSecOps practices help address vulnerabilities before deployment, while ready-made infrastructure enables scale without reinventing governance from scratch. For developers and IT leaders, this shift means moving beyond writing code to guiding the rules and safeguards that shape autonomous systems. In a fast-changing landscape, low-code provides the flexibility and resilience needed to experiment confidently, embrace innovation early, and maintain trust as AI grows more autonomous.
Author: Rodrigo Coutinho, Co-Founder and AI Product Manager at OutSystems
(Image by Alexandra_Koch)
See also: Agentic AI: Promise, scepticism, and its meaning for Southeast Asia
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