Governing Agentic AI: How to Balance Autonomy, Accountability & Enterprise Control

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 — and lived through — it’s now stitched into 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 keeps people awake at night — is agentic AI. These aren’t just predictive models or scripted automations; they behave like adaptive agents, plug into systems and people, and directly influence business-critical outcomes.

The upside is huge. Picture agents that proactively resolve customer issues in real time, or that rewire app behaviour when priorities shift. Exciting, right? But more autonomy brings fresh governance, safety and accountability complexity. This piece walks through practical approaches to capture agentic AI’s upside while keeping the levers of control where they belong — and why low-code platforms are one of the most pragmatic governance strata you can adopt.

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 as 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 feels different:

• Scale of influence: Agents can touch huge parts of your stack and workflows very quickly. One policy slip can cascade.
• Unpredictability: Adaptation introduces variance — the same prompt or 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 the trenches, the common failures aren’t usually eye-catching hallucinations. They’re slow organisational drift. Agents nudge processes off course. Logic duplicates. "Agent sprawl" becomes a thing — a real operations headache — and security exposures surface only after they’ve been exploited.

Concrete failures I’ve seen:

• Compliance gaps — an agent touches regulated data in unapproved ways and you suddenly have a reporting nightmare.
• Security exposure — automation without tight credential and API controls widens the attack surface: more hooks, more risk. (If you want a deeper read on the security angle, check the piece on 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 change how teams build: less time hand-coding every edge case, more time thoughtfully defining guardrails and oversight policies. That doesn’t mean bureaucracy — it means designing safety into the product, intentionally.

• 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 practical patterns 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 to auditors or stakeholders.
• Human-in-the-loop (HITL) — for high-risk decisions, require human sign-off or at least a quick post-action review. Often the human is the safety net, not the bottleneck.

A quick example from practice: a support agent authorised to issue refunds should have clear monetary limits, log the rationale and the data used, and record which staff member reviewed the case. If it requests more than the threshold, it escalates. Simple checks like this 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 trust invest heavily in observability — the ability to pause, rollback or constrain an agent’s behaviour when things go sideways.

Capabilities to require or build:

• Traceable decision logs — timestamped actions, the chain of reasoning or model outputs, and connecting metadata. These need to be 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 readable for non-ML folks so they can see when something smells off.

Why low-code platforms can accelerate safe scaling

Rebuilding governance from scratch is expensive and slow. Low-code platforms provide a very 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 with this approach — and 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 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 — pragmatic engineering and good choices.

Operational recommendations: how to get started

Here’s a practical roadmap I’d hand to a team deploying agentic AI. It’s not exhaustive but it’s actionable — the kind of checklist you can actually use.

• 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 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. It’s as much ops as engineering.

Balancing innovation and responsibility

Agentic AI can deliver big wins in speed, customer experience and efficiency. But success isn’t about being bold for the sake of it — it’s about being disciplined. The best teams mix rapid experimentation with uncompromising governance. They move fast — yes — 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, organisations 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 safeguards first, instrument every action, and keep humans squarely in the loop for the decisions that matter most. When the stakes are real, you want 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/).