By Yousef Khalili
Agentic AI is slowly changing the very foundations of business by allowing systems to act autonomously, make their own decisions and pursue their own goals over time, without requiring constant human intervention. The actual narrative behind this change is not the mediums that drive it but the companies intelligent enough to adapt to it.
We are now in a new period of artificial intelligence development which is no longer just assistive but autonomous. Generative AI wowed the world with what it could create, but Agentic AI is slowly changing the very foundations of business by allowing systems to act autonomously, make their own decisions and pursue their own goals over time, without requiring constant human intervention. The actual narrative behind this change is not the mediums that drive it but the companies intelligent enough to adapt to it.
What is Agentic AI?
Agentic AI is the system that exhibits agency: it can perceive its environment, develop goals, make decisions, learn in real time and is able to perform complex tasks with minimal supervision. In contrast to either traditional automation or even generative AI models, Agentic AI agents are long-lasting, goal-seeking and able to coordinate across domains, tools and timescales.
In other words: if generative AI was a talented assistant, Agentic AI is a reliable operations manager. It never waits to be told what to do, it anticipates, plans and acts.
The technology providers (such as OpenAI, Google, or xAI) are the ones stealing the headlines, but the more interesting developments are brewing behind the scenes in the boardrooms of the business world. Smart businesses are adopting agentic systems to address real-world operational challenges.
There are five lessons we can learn from the early adopters of the Agentic race.
Lesson 1: Give Agents A Mission, Not A Script
Case Example: Siemens Energy
Previously, AI in energy management was equivalent to a calculator – capable of computing when you feed it what to compute. Siemens Energy did something way more interesting. They incorporated Agentic AI in their grid monitoring systems and turbine diagnostics. These agents are not enforcing a set of rules, they are picking up deviations, setting priorities on maintenance schedules and synchronizing with multiple plants, all in real time.
The lesson? Companies that view AI as a co-pilot rather than a tool, realize more returns. Siemens did not feed the system with tasks. They gave it a purpose: to maintain the grid, keeping it stable and efficient. The agent manages the rest autonomously.
Lesson 2: Prepare Agents to Navigate ComplexityÂ
Case Example: Unilever
Unilever has quietly added agentic AI into some parts of its global supply chain. The AI agent is expected to maintain supply resilience across 190 countries. It not only reacts to demand shifts; it predicts shortages, changes logistics, negotiates contracts, and even tells procurement officers about sustainable alternatives. This is not a simple and linear optimization.  We are talking about multi-variable decision-making in action, mapping environmental, geo-political, and economic signals.
Lesson two is about scope: when you constrain your AI agent to low-level tasks, you are not ready to play on agentic scale. Let it handle real-world uncertainty.
Lesson 3: Let Agents Truly Act
Case Example: DBS Bank (Singapore)
DBS Bank has shifted towards autonomous financial agents that are taking action beyond predictive analytics. For example, one of their agents follows real-time financial behavior across accounts, identifies risk indicators, and, on its own, freezes transactions or changes portfolio exposure within pre-approved guardrails, without any human involvement.
It doesn’t simply warn a risk officer. It acts.
Most companies fail at this point. They introduce AI dashboards and forget the last-mile execution. Lesson: Companies don’t get exponential value from agentic AI when they merely embed it in thinking loops. They need to embed it in action loops.
Lesson 4:Â Â Get Agents To Orchestrate Ecosystems
Case study: Lufthansa Group
Airlines are one of the most complicated operational organizations across the globe. Lufthansa is trying out agentic AI to organise crew rostering, aircraft maintenance, weather rerouting and passenger re-accommodation – all with the same objective: minimum disruption.
In this case, the agent is a conductor rather than an analyst. It extracts information in siloed systems that previously were not communicating with one another, models results and implements strategies.
The lesson: agentic systems perform best when you abandon thinking departmentally and orchestrate change at the mission level.
Lesson 5: Make It All Explainable
Case Example: Roche pharmaceuticals
Trust is fundamental in healthcare and life sciences. Roche is using agentic AI in drug discovery and clinical trials tracking. However, their agents are designed differently. Unlike black-box AI systems, they can produce reasoning and audit trails. So in the event of a clinical recommendation being flagged, the system can explain why.
Lesson: Agentic AI must not be synonymous to mysterious AI. Transparency should be given priority from the onset. Explainability is a must in highly regulated industries like healthcare.
What Do These Companies Have In Common?Â
These companies did not wait for agentic AI to offer the perfect solution before they implemented it. They had a clear mission, they trusted their agents to evolve over time, and they restructured their business around real results.
The race has started. Those who move fast will end up not only with better AI, but with an entire new operating system for their business.
Yousef Khalili
