Agentic AI represents a major shift in how organisations will operate in the coming years. Beyond analysing data or generating content, these systems can independently set goals, plan tasks and execute actions with minimal human input. They can promote products, reallocate resources and adjust prices without approval. The promise is faster decisions, streamlined operations and greater adaptability across retail, hospitality and the public sector. Yet, as adoption accelerates, certain sectors are increasingly relying on these autonomous toolswithout implementing firm guardrails, exposing them to significant risks.
What Agentic AI Actually Is
Agentic AI covers three broad maturity levels:
With each level of autonomy, there are benefits but also risks. These agentic systems increasingly operate through continuous decision loops resembling the OODA (Observe, Orient, Decide, Act) framework used in adversarial environments. Because these loops depend on inputs that may be incomplete, biased or malicious, greater autonomy also brings greater vulnerability at every OODA stage.
Rapid Adoption, Uneven Guardrails
Across industries, organisations are experimenting with agentic AI at speed. Retailers are deploying autonomous decision-making to react to demand shifts; hotels are deploying agents to personalise guest journeys; governments are piloting AI assistants to accelerate citizen services. However, many implementations still lack clear boundaries, robust data controls, or structured accountability frameworks. This gap increases the risk of unintended or unsafebehaviour, particularly if systems are fed incomplete or unverified data.
Many organisations are now running agents whose OODA loops ingest unvalidated signals such as market movements, customer inputs or open-source online data. Because these observations are not authenticated, even subtle manipulation can corrupt an agent’s entire reasoning and decision chain.
Retail Sector: Speed and Sensitivity
Retailers are leveraging agentic AI to manage pricing, forecast demand and optimise inventory. A leading grocery chain, for example used an agent to identify a heatwave and boost promotions for cooling products within 90 minutes, driving real uplift.
However, without controls, autonomy introduces vulnerabilities:
Retailers entering 2026 will require clear and enforceable business rules, oversight for sensitive decisions and tighter controls around how customer and supply-chain data is shared.
Hospitality and Travel Sector: Personalisation or Exposure
Hotels and travel operators are rapidly adopting agentic AI for check-in, room allocation, upgrades and upselling. Back-office agents coordinate housekeeping, staffing and dynamic pricing, improving efficiency and guest experience.
But without safeguards, these systems can cause:
Operators need clearer visibility into how agents make decisions, with clear staff override pathways. Identity controls, data segmentation and real-time monitoring are essential as decision-making becomes more autonomous.
Public Sector: High Potential, Higher Stakes
Governments are exploring agentic AI for case triage, benefits processing, licensing, and citizen services. The potential benefits including improved responsiveness and reducedadministrative burden are substantial.
But risks are significantly higher:
Public-sector organisations must prioritise governance frameworks including ethical impact assessments, fairness testing, data-quality investment and clear documentation of accountability pathways long before deploying autonomy at scale.
Security and Governance: The Non-Negotiable Foundation
To deploy agentic AI responsibly, organisations must embed controls across every layer:
1. Data Management: High-quality, well-governed data is essential. Poor data leads directly to poor decisions at scale. Data protection controls cannot be waived or bypassed to accelerate AI deployments.
2. Model & Agent Design: Define boundaries, auditing capabilities and clear communication channels so that their actions are understandable and reversible. Deep monitoring, including anomaly detection, must be baked in at the design stage.
3. Access & Permissions: Apply least-privilege access, time-bound permissions, and segmentation so no agent has any unnecessary operational power. Continuous verification should be the standard, along the lines of a zero trust approach.
4. Operational Governance: Define which decisions require human review, and ensure that these process elements cannot be bypassed under any circumstances.
5. Accountability: Mapped responsibilities to data owners, model owners, business leaders and governance boards. Autonomy does not remove human accountability.
The Way Forward
Agentic AI marks a pivotal shift in organisation productivity: delivering faster decisions, scalable operations and proactive services. But autonomy without guardrails is a risk multiplier.
The OODA loop problem underscores this clearly: speed is not an advantage if an agent observation, orientation or decision can be corrupted. Ensuring semantic integrity, verifying not only data but also its trustworthiness, successful screening from prompt injection attacksand context is now essential.
The organisations that win in 2026 and beyond will adopt agentic AI built on transparency, oversight and trust, systems that extend human capability rather than act beyond it.
