Agentic AI Could Be Your Next Digital Colleague

In business, an Agent is someone who is authorised to act for you or your company. If you are a professional athlete, you may have an agent represent you in negotiations with a sports team or sponsors. A business may hire an agent to take care of a particularly complex piece of business in which that agent has a speciality. An FBI agent in the US can investigate a crime and make an arrest, without having to go back to the federal government every time to confirm.

You hire an agent because of their proven ability to make decisions and take actions on your behalf, without having to come back to you and seek confirmation every time. Importantly though, they should always have some kind of oversight.

The federal agent has a more senior agent who oversees their work. A business agent or sports agent is often a member of a trade association who will oversee that they comply with certain standards. Governments put laws in place to protect both parties, and have their own agents to enforce them. That oversight engenders trust.

When we talk about something being “agentic”, we talk about it being able to set goals, make decisions, and take actions independently to achieve an outcome. In artificial-intelligence terms, an agentic AI is a software entity that can perceive a situation, decide what to do next, and execute the steps, often by calling other AIs, systems or services, without a human telling it precisely how to do every stage.

When ChatGPT first appeared in late 2022, we typed in a text question and received the answer back in text. Fast forward to today, and this straightforward chat AI engine has grown more agentic. It can take actions on our behalf. As an example, you can ask it to generate an image of something, and behind the scenes it will craft a prompt and call an image generator (their service is called DALL-E) to create that picture. In this case, ChatGPT is acting as our agent, delegating part of the task to another AI service. This ability to use tools and other AI systems independently is at the heart of Agentic AI.

In simple terms, Agentic AI refers to AI systems (agents) that can make decisions and perform tasks autonomously toward a goal, without needing step-by-step human instructions. These agents can perceive, reason, act, and even learn from outcomes with minimal human oversight. Crucially, they don’t just generate content, they can also take actions. Your favourite chat AI might recommend the best time to climb a mountain, but if it is connected to the correct AI agent, it could also use that information to actually book your flights and hotel for the trip, usually by calling multiple other external tools.

As agentic AI moves AI beyond static question-and-answer roles into more active problem-solving roles and orchestrating tasks across various services to achieve our objectives, imagine those orchestration abilities aimed at your business workflows. From booking a restaurant, to translating legal contracts, triaging service tickets, or rebalancing an investment portfolio. That is the promise of agentic AI.

Four converging trends make 2025 a tipping-point:

• Large language models (LLMs) provide the reasoning and language skills agents need to understand messy, real-world instructions.
• Small language models are providing a high level of capability in specific domains, as well as being quicker and cheaper to run.
• Tool integration frameworks (API gateways, enterprise plug-ins, RAG pipelines) let agents trigger actions in CRMs, payment rails, or ERP stacks as easily as generating text.
• Pressure for efficiency and personalisation is pushing every industry to automate routine work while delighting customers.

Gartner predict that one-third of enterprise applications will ship with agentic capabilities by 2028. With several other reports showing over half of large firms experimenting with autonomous agents today, it may be even be more than one-third and in a shorter space of time.

The potential business value is significant, spanning efficiency gains, better customer experiences, and new revenue opportunities.

Customer Service is one business area where bots have traditionally been deployed to deflect customers from expensive human agents. The ability to automate complex tasks now lets us think about moving away from simply answering FAQs with scripted flows to having our AI agents diagnose intents and then automatically trigger the right workflow and close out tickets. Of course, they will also be able to escalate to humans where needed, but these will become edge cases.

We can even imagine that our new customer service agent can reach out to a customer before they even know that there is a problem! Imagine a telecom operator having autonomous agents monitoring signal and actioning issues as they occur. Even contacting customers to apologise before they even know that their signal was down. This not only deflects from customers contacting a call centre, it also deflects from customers taking to social media to complain about poor service!

For businesses interested in leveraging agentic AI, the best approach is often to start with targeted pilot projects. Rather than attempting to overhaul everything at once, companies are picking high-impact, manageable use cases to introduce AI agents. Common starting points include customer service bots that handle simple inquiries or IT helpdesk agents that can troubleshoot employee tech requests by themselves.

These early deployments build familiarity with how AI agents work in practice and allow organisations to develop the necessary governance (ensuring the AI stays within approved bounds, data privacy is maintained, etc.) and understand where a human needs to be in the loop. It is important to instrument everything and capture telemetry on decisions, accuracy, latency, and user sentiment. This logging is vital for audit trails and continuous learning. As comfort grows, and you build the accountability and trust, the scope of the agents can expand to more complex tasks and decisions.

Many of our most used enterprise software providers are also embedding agentic AI features into their platforms, meaning businesses might get some of these capabilities “out of the box” very soon. For example, a CRM system might come with AI agents that can automate lead follow-up or a project management tool might include an AI scheduler that coordinates team tasks proactively.

We need to remember the humans here. The goal is augmentation, not headcount reduction. Communication, change management, and training of staff to use these tools is critical (and mandatory!). Have your people become part of the team that will supervise, debug, and shape agent behaviour. Then your front-line workers become AI conductors orchestrating fleets of digital colleagues.

It is clear that AI agents will soon be as normal as any other software we use. Early adopters gain faster service, lower costs, and stickier relationships. The laggards risk being out-served by competitors whose AI never sleeps.

For leaders, the strategic question is no longer “Will we use agentic AI?” but “Where can we safely deploy our first agent to unlock real value this quarter?” Start small, learn fast, and scale with care. Your future colleagues may not sit at desks—but they will be tirelessly working alongside you all the same.