From Automation To Autonomy: Building Trust In The Age Of Agentic AI

Adopting artificial intelligence (AI) is no longer a future goal—it’s today’s reality. Across industries, organizations are incorporating AI directly into workflows, structures, and processes, with over three-fourths (78%) using AI in at least one business function. We are now at AI’s next major leap: agentic AI.

Unlike traditional AI, which assists humans with insights or limited tasks, agentic AI introduces autonomous agents capable of taking on more complex responsibilities. These systems do more than follow prompts—they interact independently with enterprise systems, adapt to changing inputs, connect with other agents, and even support business-critical decision-making processes. By introducing autonomy into enterprise systems, agentic AI empowers organizations across many use cases, departments, workflows, and data. Imagine agents that proactively resolve customer issues in real time or adapt applications on the fly to meet shifting business priorities.

However, autonomy also brings risk. Without the proper oversight, agentic systems can drift from their intended purpose or make decisions that conflict with business rules, regulations, or ethical standards. As this new era of technology begins, it will require an increased level of control. Human guardrails, platform-level governance, and transparency are now essential considerations. The opportunity presented by agentic AI is enormous, but so is the responsibility once it’s implemented. One approach to consider is working with low-code platforms. They are uniquely positioned to provide this necessary supervision, acting as a control layer between agentic AI and enterprise systems. Low-code platforms increase confidence that AI-driven processes support strategic objectives—without introducing unnecessary risk.

The New Developer Mindset: Adjusting to Agentic AI

Agentic AI goes beyond answering questions or typing your next email—it’s indicative of a fundamental change in the relationship between people and software. Traditionally, developers have focused on building applications with clear requirements and predictable outputs. Now, instead of piecemeal applications, teams will orchestrate entire ecosystems of agents that interact with people, systems, and data.

This evolution elevates the developer role. No longer just coders or implementers, developers become strategic orchestrators, guiding how agentic AI interacts with people, data, and business processes. Their work becomes less about writing every line of logic and more about designing the rules, guardrails, and governance structures under which AI agents operate.

While this unlocks new levels of efficiency and responsiveness, it also requires a different mindset around how software is developed and governed:

• Agentic systems are non-deterministic. They do not always produce the same outcome for the same input because they reason and adapt.
• Transparency and traceability are non-negotiable. If an AI-driven process rejects a loan application or reprioritizes logistics, leaders need to understand why.
• Governance and compliance are essential. Systems must be designed with oversight, auditability, and compliance in mind from day one, not as an afterthought.

In short, the move towards agentic AI demands that developers and IT leaders embrace a broader supervisor role, guiding both technology and organizational change over time.

The Governance Gap: New Risks, New Responsibilities

With autonomy comes new 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 guardrails, 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.  These are not abstract issues—they point to concrete risks we’ll explore in the sections ahead.

Lack of decision transparency and human control

Autonomous agents may make decisions that are unclear to business leaders. Without built-in mechanisms for visibility, enterprises risk losing accountability in critical workflows. Critical decisions demand a human in the loop. For example, if an agent autonomously approves transactions, who is responsible when an error occurs? Remember, AI cannot be held accountable.

Security vulnerabilities from autonomous behavior

Autonomous agents interact with internal systems, application programming interfaces (APIs), and sensitive data, making them high-value targets for cyber threats. A compromised agent could wreak havoc across multiple systems in ways that are far harder to detect than traditional breaches.

Risk of AI sprawl

Multiple unmonitored autonomous agents can proliferate, creating fragmentation, redundancy, inefficiency, and uncoordinated decision-making across development organizations. Without a governance framework, this “agent sprawl” undermines both trust and effectiveness.

Together, these risks can not only stall innovation but also cause far greater harm before organizations realize AI’s full benefits. However, they can be mitigated with a secure, governed, and end-to-end platform that ensures compliance, transparency, and operational discipline from the outset.

Why Low-Code is Built for this Moment

The leap to agentic AI does not have to mean reinventing governance frameworks from scratch. Organizations 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.

For example, as AI agents are becoming more prevalent, IT teams are increasingly tasked with deploying them across enterprise operations. With the right frameworks, IT teams can deploy AI agents directly into enterprise-wide operations without disrupting current workflows or rearchitecting core systems. Organizations have full control over how AI agents operate at every step, ultimately building trust to scale confidently across the enterprise.

Some of the unique capabilities low-code brings to agentic AI include:

• App and agent delivery with built-in governance: Developers can build and manage both applications and agents in a single environment, with governance as part of the workflow.
• Full-stack integration: AI agents can interact seamlessly across different layers of enterprise systems, from front-end interfaces to back-end processes.
• Built-in DevSecOps: Low-code platforms embed security practices directly into the development cycle, ensuring vulnerabilities are addressed before deployment.
• Out-of-the-box infrastructure: Teams can scale without spending months reinventing foundational elements of compliance or oversight.

In practice, enterprises can experiment with agentic AI, deploy it at scale, and remain confident that compliance and security guardrails are intact. Low-code makes it easier to deliver with speed and security, giving developers and IT leaders confidence to move forward.

Smarter Systems Need Smarter Oversight

Low-code frameworks don’t just help organizations build faster—they help them build smarter. With governance baked into the development process and controls designed for scalability and compliance, teams can confidently integrate agentic AI into their systems without sacrificing oversight or trust.

For developers and IT leaders, this represents a profound transformation of their place within the organization. They move from simply writing every line of code to setting the rules, guiding the agents, and shaping how software behaves at scale.

The pace of innovation isn’t slowing down. We can expect continual waves of new technologies and agentic capabilities, each iteration promising fresh opportunities but also introducing potential risks. In this environment, organizations need platforms that are flexible, iterative, and built for resilience. Approaches such as low-code provide a way to experiment with emerging tools like agentic AI while maintaining market competitiveness and the agility to be early adopters. By leveraging these frameworks, enterprises can embrace rapid change without incurring long-term technical debt, sacrificing product quality, or exposing themselves to risks that could undermine future success and reputation.