Unlocking the Future: The Role of Agentic AI in Business Transformation

Author: Rodrigo Coutinho, Co-Founder and AI Product Manager at OutSystems

The Rise of AI in Business

Artificial Intelligence (AI) has transitioned from the realm of pilot projects and theoretical applications to becoming a core component of modern industries. Recent statistics reveal that over three-quarters of organizations—specifically 78%—are utilizing AI in at least one business function. This shift signals a pivotal moment in the evolution of AI, paving the way for the next generation: agentic AI.

What is Agentic AI?

Agentic AI encompasses systems that extend beyond providing insights or automating narrow tasks. These advanced agents function autonomously, adapting to dynamic inputs, integrating with various systems, and influencing crucial business decisions. While the potential benefits of agentic AI are immense, they also introduce unique challenges that organizations must navigate.

The Promise and Challenges of Agentic AI

Picture AI agents that can proactively resolve customer issues in real time or adjust applications dynamically to align with changing business priorities. However, this increased autonomy can lead to new risks. Without appropriate safeguards, AI agents may deviate from their intended functions or make decisions that conflict with business rules, regulations, or ethical standards. Thus, effective governance is essential, requiring organizations to incorporate human judgment, robust governance frameworks, and transparency from the outset.

Designing Safeguards Instead of Code for Agentic AI

Agentic AI represents a significant shift in how humans interact with software. Traditionally, developers focused on building applications with explicit requirements and predictable outputs. In contrast, the advent of agentic AI necessitates the orchestration of entire ecosystems of agents interacting with each other and with humans.

As these systems evolve, developers will transition from writing line-by-line code to defining the safeguards that guide these agents. Given the adaptive nature of agentic AI, transparency and accountability must be embedded from the beginning. By integrating oversight and compliance into the design process, developers can ensure that AI-driven decisions remain reliable, explainable, and aligned with overarching business objectives.

The Importance of Transparency and Control in Agentic AI

With greater autonomy comes increased vulnerability for organizations. According to a recent study by OutSystems, 64% of technology leaders identify governance, trust, and safety as their top concerns when deploying AI agents at scale. Insufficient safeguards can lead to compliance issues, security breaches, and reputational damage. The opacity of agentic systems complicates decision validation, undermining trust both internally and externally.

If left unchecked, autonomous agents can blur accountability, broaden the attack surface, and create inconsistencies across systems. A lack of visibility into an AI system’s actions risks accountability in critical workflows, particularly when sensitive data is involved. This highlights the urgent need for strong governance frameworks that uphold trust and control as autonomy increases.

Scaling AI Safely with Low-Code Foundations

Adopting agentic AI does not necessitate a complete overhaul of governance structures. Organizations have various strategies at their disposal, including low-code platforms, which provide a reliable and scalable framework where security, compliance, and governance are integral to the development process.

IT teams are increasingly tasked with embedding agents into operations without disrupting existing workflows. With the right frameworks, AI agents can be deployed directly into enterprise operations, preserving the functionality of current systems. This approach allows organizations to maintain control over AI agent operations at every phase, fostering trust as they scale their AI capabilities.

Why Low-Code is Key for Governance and Security

Low-code development places governance, security, and scalability at the forefront of AI adoption. By consolidating app and agent development in a unified environment, it simplifies the integration of compliance and oversight from the start. The ability to seamlessly integrate with existing enterprise systems, combined with built-in DevSecOps practices, ensures potential vulnerabilities are addressed prior to deployment.

This approach allows organizations to pilot and scale agentic AI while maintaining rigorous compliance and security standards. Low-code accelerates delivery with speed and security, empowering developers and IT leaders to confidently advance their AI initiatives.

Conclusion: Embracing the Future of AI with Confidence

Ultimately, low-code platforms provide a dependable pathway to scaling autonomous AI while preserving trust. By integrating app and agent development within a single environment, low-code facilitates compliance and oversight from the outset. The seamless integration of systems and built-in DevSecOps practices address vulnerabilities proactively, enabling organizations to scale efficiently without needing to reinvent governance frameworks.

As the landscape of AI continues to evolve, the shift from coding to guiding the rules and safeguards shaping autonomous systems is paramount. Low-code platforms equip organizations with the flexibility and resilience to experiment confidently, embrace innovation, and maintain trust as AI becomes increasingly autonomous.

Engagement Questions

• What is agentic AI, and how does it differ from traditional AI?
  Agentic AI refers to autonomous systems that can adapt and influence decisions, as opposed to traditional AI, which typically provides insights or automates tasks.
• What are the primary risks associated with deploying agentic AI?
  The main risks include governance gaps, security vulnerabilities, and the potential for decisions that conflict with business standards.
• How can organizations ensure accountability in AI-driven decision-making?
  By embedding transparency and oversight into the design process, organizations can maintain accountability and trust in AI systems.
• Why are low-code platforms essential for scaling AI?
  Low-code platforms integrate security, compliance, and governance into the development process, facilitating quicker and safer deployment of AI agents.
• What role do IT teams play in the deployment of agentic AI?
  IT teams are responsible for embedding AI agents into existing workflows, maintaining operational integrity while scaling AI capabilities.

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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