2025 – The year of the AI Agent

An explosion of AI apps is plain to see in 2025, with applications for productivity, development, and revenue cycle management. That explosion is made possible by rapidly improving models and the underlying platform infrastructure built over the past 24 months, which simplified hosting, fine-tuning, data loading, and memory — and made it easier to build apps.

But the blistering pace of AI development means few assumptions hold true for long. Apps are now being built in a new way that will impose new requirements on the underlying infrastructure. Those developers are speeding across a half-finished bridge.

Their apps won’t achieve their full potential if our industry fails to support them lower in the stack with a new set of AI agent Infrastructure components.

It’s the time of year to consider technology in the new year – specifically technology that could change how businesses work. In 2025, that discussion should continue to include artificial intelligence.

During the past two years, AI has become universally discussed in our society. The problem is that while employees and business owners talked, there are opposing opinions about how AI will impact their lives.

This article puts forward ideas about how businesses can use AI to streamline operations, enhance customer engagement, and increase productivity – all while continuing to use the same technology investments in place now.

Technology is a progression, so we start this exploration with chatbots and applications, and then add A.I. in the conversation. We also discuss why AI agents need the technology investments that your business is currently making.

Chatbots: Conversational Interfaces

A chatbot is designed to simulate conversation with users. They operate primarily via text or voice on websites, messaging apps, or customer support systems.

Chatbots are are generally rule-based which means that they operate on predefined scripts relying on specific keywords or phrases to respond. A key attribute is that they are limited in flexibility. For instance, a customer service bot might provide options like “Press 1 for billing” or “Type ‘support’ for help.”

Some chatbots use natural language processing (NLP) and machine learning to understand and respond more dynamically. Examples include virtual assistants like Siri or Alexa, which can handle varied queries and adapt over time.

Chatbots are generally task-oriented, and are generally used for:

  • Customer Support: Answering frequently asked questions.
  • Lead Generation: Gathering user data through friendly interactions.
  • Process Automation: Scheduling appointments or managing simple workflows.

Rule-based systems are rigid and can frustrate users when they encounter unexpected queries. Even the more advanced chatbots can still struggle with complex conversations or context beyond their training.

Applications: Multifunctional Solutions

Applications (apps) are designed for specific purposes, and are used in most industries for banking, accounting, project management, and specialised usecases. Apps are user-driven and require interaction via graphical user interfaces (GUIs) like buttons, menus, and forms.

Applications offer extensive functionality, often tailored to specific industries:

  • Productivity Apps: Tools like office suites or Slack for collaboration.
  • E-Commerce Apps: Platforms like Amazon for online shopping.
  • Enterprise Solutions: Systems like OdooERP for managing operations.

Applications excel in delivering robust functionality but often have steep learning curves. Users must navigate interfaces, understand workflows, and sometimes use multiple apps to complete a task. This fragmentation can reduce efficiency and lead to “app fatigue,” where users struggle to manage numerous tools.

Which leads us to AI Agents: Intelligent, Autonomous Assistants

AI agents are intelligent systems designed to perform tasks autonomously.

They combine machine learning, NLP, contextual understanding, and predictive analytics to understand, learn, and act in real-time to proactively make decisions and execute tasks without human intervention.

When creating AI Agents, it makes sense to start with the existing technology platforms that already exist – chatbots.

AI agents combine the functionality of chatbots and applications by integrating conversational abilities with autonomous functionality:

  • Contextual Understanding: AI agents leverage contextual data—user history, preferences, and real-time inputs—to offer more relevant and accurate responses.
  • Multimodal Capabilities: Integrating voice, text, and visual recognition allows AI agents to understand and respond across different environments/formats.
  • Integration with Ecosystems: AI agents connect with other systems and applications, enabling seamless task execution.
  • Personalization: Adapting behavior based on user preferences and historical interactions.
  • Proactive Assistance: Reminding users about deadlines, predicting needs, and suggesting actions.

Examples of AI Agents include:

  1. tools to write reports, manage calendars, and even simulate human-like reasoning.
  2. advanced customer support, such as automatically requesting documents or additional information based on the type of request, within the same conversation.
  3. dynamic pricing systems, and
  4. personalized marketing strategies.

Why AI Agents need your Business Applications

The concept of what makes an AI Agent is still being defined. TechCrunch has a good article on the challenges of defining an AI Agent, and some of the industry issues facing the developers.

But the simple fact is that AI Agents require access to information used to allow them to form a response. For a business, the data comes from your current knowledge-bases, and application databases. If the data is not available, then the response will not be helpful. Connecting data from multiple applications addresses the shortcomings of current app-based ecosystems. 

 So the message here is that getting your business data is necessary before you can design or create an AI Agent. 

The Future of Business Technology

The transition from chatbots and applications to AI agents reflects the broader trend toward intelligent, adaptive systems. Businesses are increasingly looking for solutions that simplify operations, enhance user experiences, and deliver measurable outcomes. While chatbots and traditional applications have played crucial roles in the digital transformation journey, AI agents represent the next frontier.

AI Agents solve many current technology based problems, such as streamlining the user experience by integrating diverse functionalities into a single, unified interface. Instead of toggling between multiple apps for email, project management, and analytics, an AI agent can handle all tasks within one conversational framework.

Users can simply speak their goals, and the AI agent determines the best approach/data source to achieve them.

In the coming year, the discussion about AI agents will dominate the discussion about AI. Their ability to combine conversational ease with autonomous decision-making will redefine how businesses operate, compete, and innovate. As companies embrace these advanced systems, human and machine collaboration will continue,  paving the way for enhanced customer experiences, cost saving by automating complex workflows and increased operational efficiency.