Author: Rodrigo Coutinho, co-founder and AI product manager of OutSystems
AI is moving beyond pilot projects and future promises. Today, it is embedded in the industry, with over three-quarters (78%) of organizations using AI in at least one business feature. However, the next leap forward is Agent AI. It can not only provide insights and automate narrow tasks, but it can act as an autonomous agent, adapt to input changes, influence connections with other systems, and business critical decisions. These agents bring greater value, but Agent AI also raises challenges.
Imagine an agent who actively solves customer problems in real time. More autonomy inevitably poses new risks. Without a proper safeguard, AI agents may choose to drift away from their intended purpose or conflict with business rules, regulations, or ethical standards. Navigating this new era requires stronger surveillance, with human judgment, governance frameworks and transparency built into the ground. The possibilities for Agent AI are vast, but so are the obligations associated with deployment. The low-code platform provides one path that acts as a control layer between an autonomous agent and an enterprise system. By incorporating compliance into governance and development, they give organizations the confidence that AI-driven processes will advance their strategic goals without adding unnecessary risks.
Design a safeguard instead of agent AI code
Agent AI is showing a sudden change in the way people interact with software. This illustrates a fundamental change in the relationship between people and software. Traditionally, developers have focused on building applications with clear requirements and predictable output. Now, instead of fragmented applications, teams coordinate an entire ecosystem of agents interacting with people, systems, and data.
As these systems mature, developers move from writing codelines line by line to the definition of safeguards that guide them. Transparency and accountability must be built into the beginning, as these agents can adapt and respond differently to the same input. By incorporating monitoring and compliance into the design, developers ensure that AI-driven decisions are reliable, explainable and aligned to business goals. This change requires developers and IT leaders to embrace the broader role of supervisors and guide both technical and systematic changes over time.
Why Agent AI transparency and control are important
Greater Autonomy exposes organizations to additional vulnerabilities. A recent OutSystems survey found that 64% of technology leaders appeal to governance, trust and safety as the biggest concerns when deploying AI agents at scale. Without strong safeguards, these risks go beyond the compliance gap and include security breaches and reputational damage. The opacity of agent systems makes it difficult for leaders to understand or validate decisions, erode internal and customer trust, leading to concrete risks.
The left-hand agent of unchecked autonomous agents can obscure accountability, broaden the attack surface, and create contradictions on a large scale. Without visualizing why AI systems work, organizations risk losing accountability in critical workflows. At the same time, agents interacting with sensitive data and systems extend the attack surface due to cyber threats, but unsupervised “agent sprawls” can produce redundancy, fragmentation and inconsistent decisions. Together, these challenges highlight the need for a strong governance framework that maintains trust and control as a measure of autonomy.
Scaling AI safely on low-code foundations
Importantly, recruiting agent AI doesn’t require restructuring governance from scratch. Organizations can utilize multiple approaches, including low-code platforms that provide a reliable, scalable framework where security, compliance and governance are already part of the development fabric.
Across the enterprise, IT teams are being asked to embed agents in operations without disrupting what already works. With the right framework, IT teams can deploy AI agents directly to operations across the enterprise without disrupting current workflows or re-engraving the core systems. Organizations have full control over how AI agents work at every step, and ultimately build trust to scale with confidence in the company.
Low code places governance, security, and scalability at the heart of AI adoption. By integrating app and agent development in a single environment, it’s easier to embed compliance and monitoring from the start. The ability to seamlessly integrate into enterprise systems and built-in DevSecops practices help address vulnerabilities before deployment. And out-of-the-box infrastructure allows organizations to confidently scale up the fundamentals of governance and security without reinventing them.
This approach allows organizations to pilot and scale agent AI while maintaining compliance and security intact. Lowcode makes delivery easier with speed and security, giving developers and IT leaders the confidence to advance.
Smarter surveillance of smarter systems
Ultimately, low code provides a reliable route to scale autonomous AI while maintaining trust. By integrating app and agent development in one environment, low code embeds compliance and monitoring from the start. Seamless integration with systems and built-in DevSecops practices helps address vulnerabilities before deployment, but off-the-shelf infrastructure enables scaling without reinventing governance from scratch. For developers and IT leaders, this shift means moving beyond writing code to guide the rules and safeguards that form an autonomous system. In a rapidly changing landscape, low-code offers the flexibility and resilience needed to experiment with confidence, embrace innovation early and maintain trust as AI grows more autonomously.
Author: Rodrigo Coutinho, co-founder and AI product manager of OutSystems
(Image by Alexandra_koch)
See: Agent AI: Promises, Scepticism, and Its Implications for Southeast Asia
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