What is agentic AI and why does it matter for your business?
Agentic AI is the next step beyond chatbots. It does not just answer questions — it takes actions. Here is what that means in practice.
There is a distinction in AI that most business coverage glosses over, and it is one of the most important things you can understand right now.
A chatbot answers questions. You type something, it responds, and then you go and do the thing yourself. That is useful. But it still requires your time and attention to act on what it tells you.
An AI agent is different. It does not just respond. It takes actions, autonomously, across multiple steps, often connecting to other tools and systems to get things done. You describe the outcome you want, and the agent works towards it.
This distinction is the difference between a very capable assistant and an actual member of your team.
What agentic AI actually looks like
The simplest way to understand agents is through concrete examples.
A chatbot helps you draft an email. You copy it, open your email client, paste it in, address it, and send it. Five steps after the AI finishes its job.
An agent drafts the email, addresses it to the right person, attaches the relevant document from your Google Drive, and puts it in your drafts folder ready to send. You review and approve. One step.
That compression of effort is what makes agentic AI genuinely transformative rather than just useful.
Why Nvidia GTC 2026 put agentic AI front and centre
At Nvidia's GTC conference in March 2026, agentic AI was not a side topic. It was the central theme. Jensen Huang devoted significant time to explaining how AI is moving from inference (models answering questions) to action (agents doing work). Nvidia's own infrastructure announcements were built around the assumption that agentic workloads would dominate the next wave of AI compute demand.
When the world's most important AI infrastructure company structures its product roadmap around agentic AI, it is worth paying attention.
The shift is happening at both the infrastructure level and the application level. The tools are arriving faster than most businesses realise.
What this looks like in professional services
The professional services sector is well positioned to benefit from agentic AI, precisely because so much of the work involves structured, repeatable processes that require intelligence but follow predictable patterns.
A management consultant's agent
Imagine an agent that, when briefed on a new client engagement, researches the client's sector and competitive landscape, synthesises the findings into a structured briefing document, drafts a proposal outline based on the firm's standard methodology, and formats it in the firm's template, ready for the partner to review.
Each of those steps currently takes human hours. The agent completes them in minutes. The partner's job shifts from doing the research to reviewing and directing the output. That is a meaningful change in how time is spent.
A solicitor's agent
A solicitor handling a commercial property transaction might set up an agent to monitor their case management system, flag approaching deadlines, draft update emails to clients when key milestones are reached, and prompt the fee earner when a response has been outstanding for more than a defined period.
The agent does not replace the solicitor's legal judgment. It handles the administrative and communication layer that currently eats time a solicitor could spend on higher-value work.
What tools enable this today
The agentic AI landscape is developing quickly. Three areas are worth knowing:
ChatGPT write actions (available to Plus and Teams subscribers) allow ChatGPT to draft into Gmail, create Google Docs, and take actions in connected tools. Early-stage but working.
Zapier AI connects to thousands of business applications and allows natural language instructions to trigger automated workflows across your existing tools. More accessible than building custom agents, and genuinely useful for common business processes.
Claude (Anthropic) is increasingly being embedded into agentic frameworks by developers building custom solutions. Its strength in complex reasoning and multi-step tasks makes it well suited to professional services applications.
What is still early stage
Agentic AI is not fully mature. Some important caveats:
Agents make mistakes. The more steps in a workflow, the more opportunity for errors to compound. Human oversight is not optional right now, it is essential.
Integration is still complex. Getting an agent to work reliably across your specific combination of tools requires setup, and often some technical capability or support.
Trust is a genuine issue. Giving an AI agent the authority to send emails or update records on your behalf requires confidence in its reliability that many businesses are not yet ready to extend.
The right posture is: understand what is possible, experiment in controlled ways, and build the habits and the infrastructure to use these tools well as they mature.
The long view
The firms that understand agentic AI now, that are experimenting with it, building internal capability around it, and beginning to reshape their workflows with it, will be operating in a fundamentally different way to those that are not.
This is not about replacing people. It is about what the people in your firm spend their time on. The firms that use agents to handle repeatable, structured work will free up their teams for the things that genuinely require human judgment.
By the time agentic AI goes fully mainstream, those firms will have years of advantage built in. The time to start is now.
Explore more on AdaHQ
Everything you need to start using AI in your business.