Insights

What is Agentic AI?

Estimated reading time: 6 minutes.

Agentic AI has become a hot topic, but what does it actually mean for your business?

Agentic AI is the next evolution of artificial intelligence. It’s a new way of thinking about how AI can operate in your business.

Rather than a human giving an AI a specific, single task (like writing an email), an agentic AI is given a high-level goal (like “increase online sales”) and it figures out the best path to get there. It breaks down the goal into smaller steps, executes them, and even self-corrects if things go wrong.

In short, agentic AI is about business automation on a grand scale. It’s not just a chatbot or a content generator. It’s a complete reimagining of how certain tasks get done. It’s important to keep this in mind as you explore how these powerful tools can transform your business.

Generative AI versus Agentic AI

Generative AI

Generative AI, like ChatGPT or DALL-E, is a tool for creation. You give it a prompt and it generates something new, whether it’s text, images, or even code. The key here is that a human is always in the loop. You tell the AI what to do, it does it, and then you review the output. It’s a powerful and efficient assistant, but it’s not autonomous.

For example, a marketing team might use a generative AI to create social media captions for a new product launch. The human provides the product details and a prompt, and the AI provides a few options. The human then selects and refines the best one.

This is certainly a significant benefit, but it’s well short of the potential of ‘agentic AI’.

Agentic AI

Agentic AI takes this a step further by removing the need for a human to micromanage every single step.

An agentic AI can reason, plan, and execute its own actions to achieve a given goal. It can interact with other tools and systems, gather information, and make decisions along the way. It’s like giving an employee a project and trusting them to get it done, rather than giving them a single, small task.

They can now ask questions such as ‘Can we adopt technologies to drive operational efficiencies, significantly improve the customer experience or even create a new business model?’.

Examples of Agentic AI

Let’s take a look at some practical examples of agentic AI.

Agentic AI in customer service

Imagine a customer’s subscription is about to expire. An agentic AI could be tasked with the goal of “retain the customer”. It would then proactively search the customer’s history and find relevant data.

It might see that the customer frequently uses a specific feature and has been a loyal customer for years. The AI could then draft a personalized email offering a discount on a renewal, and even offer to schedule a call with a support agent to address any questions.

The key here is that the AI didn’t just write an email; it identified a problem, gathered data, and executed a multi-step plan to solve it without human intervention.

Agentic AI in marketing

An agentic AI could be given the goal of “run a social media campaign for a new product”.

It would start by researching the target audience, identifying the best platforms to use, and even generating an ad budget based on a variety of factors, including past campaigns.

The AI would then create the content (images, captions, etc.) and schedule the posts. It would also monitor the campaign’s performance in real-time and make adjustments as needed. If one ad isn’t performing well, it would automatically pause it and try a different variation.

Agentic AI in operations

An agentic AI could be tasked with “optimise our supply chain for a specific product”.

It would start by analysing real-time data from various sources, such as supplier inventory levels, shipping timelines, and customer order History.

The AI could then automatically place orders with suppliers when inventory drops below a certain threshold. It could also find opportunities to switch to a different supplier if a better price is available or if there’s a risk of a delay.

Perhaps the ultimate example is Amazon Go with their ‘Just Walk Out’ shopping experience. No check-out! Digital vision and sensors – combined with deep learning – automatically detect when products are taken from or returned to the shelves and keep track of them in a virtual cart.

A step-by-step example of introducing agentic AI to your business

Introducing Agentic AI into your SMB is a strategic process that, when done correctly, can lead to significant gains in efficiency and productivity.

It’s not about a “big bang” overhaul, but a thoughtful, step-by-step introduction of a powerful new tool.

The key is to start small, with a clear, simple task that demonstrates immediate value and builds employee trust.

Here’s a practical, step-by-step guide for introducing AI into your office by automating a common task: creating a weekly team summary.

Step 1: Identify a Repetitive, Data-Driven Task

The ideal first candidate for AI automation is a task that is:

Repetitive: It’s done at a regular interval (e.g., daily, weekly, monthly).
Data-driven: It involves gathering and synthesizing information from multiple sources.
Low-risk: A mistake won’t have a catastrophic impact on the business.

A great example is the weekly team summary. This involves a manager or team member gathering updates from various team members, and compiling them into a cohesive report. The process is often manual and time-consuming.

Step 2: Define the Goal and Success Metrics

Before you introduce the AI, you need to be crystal clear about what you want it to accomplish. For our example, the goal is:

Goal: Create a concise, accurate, and insightful weekly team summary without manual data compilation.

Success Metrics:

Reduce the time spent on creating the summary by at least 75%.
Increase the consistency and quality of the summaries.
Ensure the summary is delivered to the team by the end of the day on Friday, every week.

Step 3: Choose the Right Tool

For this task, an agentic AI tool that can integrate with your existing software stack is the best choice. Look for a solution that can connect to:

Communication Platforms: Slack or Microsoft Teams, where team updates are often shared.
Project Management Tools: Asana, Trello, or Jira, where task status is tracked.
Email: To distribute the final summary.

Many modern AI platforms offer these integrations and can be configured with a no-code or low-code interface, making them accessible even without a technical background.

Step 4: Configure the AI Agent

This is where the magic happens. You’ll be giving the AI a high-level goal and providing the “tools” it needs to achieve it. Action:

Grant Access: Connect the AI tool to your team’s Slack channel and project management software with appropriate permissions.
Define the Goal: Provide the AI with the overarching goal: “Create and send a weekly team summary to the ‘Management’ email list.”
Specify Sub-Tasks: Break down the process for the AI. You might prompt it with instructions like:
- “Every Friday at 4 PM, search the #team-updates Slack channel for messages posted this week by team members.”
- “Access our project management tool and identify all tasks marked ‘Completed’ this week.”
- “Synthesize this information into a 3-5 paragraph summary that highlights key accomplishments, identifies any roadblocks, and notes upcoming priorities.”
- “Draft an email with the subject line ‘Weekly Team Summary for [Current Date]’ and attach the generated summary.”
- “Send the email to the ‘Management’ distribution list.”

Step 5: Conduct a Pilot and Gather Feedback

Start by running the AI agent in a test environment or a small, controlled group. Don’t deploy it company-wide immediately.

Action:

For the first few weeks, have the AI draft the summary but send it to a small group of managers for review.
Compare the AI-generated summary to the manual version. Is it accurate? Is the tone correct? Does it miss any key details?
Gather feedback from the pilot group. Their input is invaluable for fine-tuning the AI’s prompts and ensuring the output is truly useful.
Keep your employees in the loop. Explain what you’re doing, why you’re doing it, and how the AI will free them up to focus on more strategic work. This transparency is crucial for building trust.

Step 6: Deploy and Monitor

Once the pilot is successful and the AI is reliably producing high-quality summaries, you can fully deploy the agent.