Learn how to scale agentic AI safely: governance, transparency, low-code foundations, and practical safeguards to balance autonomy and accountability.

Introduction: Why agentic AI changes the game

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

AI stopped being a curiosity years ago. From what I've seen, it’s now woven into product roadmaps, customer journeys and even the morning stand-ups. Recent research suggests roughly 78% of organisations use AI in at least one business function [Source: McKinsey]. The next wave — and the one that really keeps folks awake at night — is agentic AI. These are systems that don’t just recommend or execute single tasks; they behave like adaptive agents, plug into systems and people, and directly influence business-critical outcomes.

The upside is enormous. Imagine agents that proactively resolve customer issues in real time or rewire app behaviours to match shifting priorities. Exciting, right? But the flip side is real too: more autonomy means new governance, safety and accountability complexities. This piece covers practical approaches to capture the upside of agentic AI while keeping the levers of control where they belong — with a particular eye to low-code platforms as an effective governance stratum.

What is agentic AI and why it matters

Agentic AI describes systems that act with autonomy: they perceive inputs, plan, take actions toward goals and adapt when the environment changes. Unlike narrow automation or analytics, agentic systems can make decisions that materially affect customers, employees and business outcomes.

Why this matters now (and why it’s different):

• Scale of influence: Agents can touch vast swathes of your stack and workflows at speed. One policy slip can cascade.
• Unpredictability: Adaptation introduces variance — the same instruction today may yield a different, emergent action tomorrow.
• Accountability risks: When agents act autonomously, tracing who made what call and why gets harder — fast.

What goes wrong when autonomy lacks governance?

From my trenches, the most common failures aren’t headline-grabbing hallucinations but slow, organisational drift. Agents subtly change behaviour away from intended rules. Processes duplicate. "Agent sprawl" becomes a real thing. And you start discovering security exposures only after they’ve been exploited.

Concrete risks I’ve watched unfold:

• Compliance gaps — an agent touches regulated data in unapproved ways, and suddenly you’ve got a reporting nightmare.
• Security exposure — automation without tight credential and API controls widens the attack surface — more hooks, more risk. Learn more on securing agent-driven workflows in AI and cybersecurity.
• Loss of trust — opacity erodes stakeholder confidence. If people can’t explain or audit decisions, they stop trusting the system.

Designing safeguards (not just code) for agentic AI

We need to reorient how developers work: less time hand-coding every path, more time thoughtfully defining guardrails and oversight policies. That doesn’t mean bureaucracy for the sake of it — it means designing safety into the product.

• Explicit rules of engagement — spell out allowed vs forbidden actions, escalation flows and approval thresholds. Don’t assume ‘‘it will do the right thing’’. See patterns for agentic deployments in agentic workflows.
• Explainability and logging — capture decision traces: inputs, outputs, model signals and confidence scores. If you can’t reconstruct a decision, you can’t defend it.
• Human-in-the-loop (HITL) — for high-risk decisions, require human sign-off or at least a rapid post-action review. Often the human is the safety net, not the bottleneck.

Quick example from practice: a customer-support agent authorised to issue refunds should operate within clear monetary limits, log the reason and the data used, and record which staff member reviewed the case. If the agent requests more than the threshold, it escalates. Simple. But these checks catch a lot of creeping errors.

Transparency and control: the pillars of trust

Transparency is not a compliance checkbox. It’s the operational foundation of trust. Teams I respect invest heavily in observability — the ability to pause, rollback or constrain an agent’s behaviour when things go sideways.

Key capabilities to require or build:

• Traceable decision logs — timestamped actions, the chain of reasoning or model outputs used, and connecting metadata. These are audit-grade, not just debug traces.
• Policy enforcement engine — a central place to encode business rules and access controls that agents must obey. Think of it as the rulebook the agent can’t rewrite.
• Governance dashboards — surface drift, anomalous patterns and security issues with alerting and audit trails. Make it easy for non-ML folks to see when something smells off.

Why low-code platforms can accelerate safe scaling

Rebuilding governance from scratch is expensive and slow. Low-code platforms, however, provide a pragmatic alternative: an integrated environment where app logic, agent orchestration and governance primitives can live together. I’ve seen teams move from brittle prototypes to production faster using this approach — with fewer surprises.

Benefits of using low-code as a governance layer:

• Built-in compliance — many platforms already include RBAC, audit trails and secure integration patterns. You don’t have to reinvent the wheel.
• Faster iteration — codeless orchestration lets teams prototype agents and policy flows quickly, then package successful patterns into reusable components.
• Unified DevSecOps — testing and security pipelines become consistent across human and agent-driven workflows, reducing integration surprises. For how integrated security and AI intersect, see AI phishing detection.

Hypothetical, but grounded: a retail IT team pilots an agent that tweaks inventory reorders. With a low-code platform they template the workflow, embed approval gates for large orders, and hook native logging to their SIEM. Result: safe scaling with minimal re-architecture. Seen it happen. Not magic — just pragmatic engineering.

Operational recommendations: how to get started

Here’s a practical roadmap I’d hand to a team starting to deploy agentic AI. It’s not exhaustive, but it’s actionable.

• Assess impact zones — map where agents could make autonomous decisions and classify actions by risk (low / medium / high). Don’t skip this; assumptions bite you later.
• Define guardrails first — encode policies, approval thresholds and data access rules before agents go live. Protect the crown jewels first.
• Instrument observability — ensure every agent action produces audit-ready logs and an explanation you can show to auditors and product owners.
• Adopt low-code where it fits — use it to standardise governance, accelerate safe experimentation and reduce custom integration errors. Not everywhere, but where speed and control matter.
• Train supervisors — shift developers and IT into supervisor roles: monitor agent fleets, tune policies and manage escalation paths. It’s an ops problem as much as an engineering one.

Balancing innovation and responsibility

Agentic AI offers a huge lift in speed, customer experience and operational efficiency. But success isn’t about being bold for its own sake — it’s about being disciplined. The best teams I’ve worked with combine rapid experimentation with uncompromising governance. They move fast, sure — but they can always explain, pause or undo an agent’s action.

Think of governance as product, not friction. It’s a feature that enables trust and scale. When guardrails are first-class citizens, organisations can capture agentic AI’s value without exposing themselves to unacceptable risk. Simple idea. Hard in practice. But very doable.

Final thoughts

Autonomy and accountability are two sides of the same coin. To unlock agentic AI’s potential, you need technical controls, human oversight and an operational culture that prizes transparency. Low-code platforms offer a pragmatic path by embedding governance into the development fabric — helping teams experiment, scale and stay auditable.

In short: embrace agentic AI, but do it with your eyes open. Build your safeguards first, instrument every action, and keep humans squarely in the loop for the decisions that matter most. Because when the stakes are real, you want the answers — and the ability to act on them.

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

Image credit: Alexandra_Koch (Pixabay)

Further reading: McKinsey - The State of AI (https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai). For a recent study on governance concerns, see OutSystems agentic AI study (https://www.outsystems.com/news/agentic-ai-study/).