Somebody call my agent!
“AI agents have become the newest front in the battle between tech companies as they look to drive revenues from the fast-developing technology. OpenAI is betting that artificial intelligence-powered assistants will “hit the mainstream” by next year as tech groups, including Google and Apple, race to bring so-called AI agents to consumers” [1]
A recent slew of press articles have suggested that AI Agents are the latest manifestation of disruptive AI based technologies. So, what is an AI Agent? And how does this differ from an AI Chatbot that we might have come across?
Let’s tackle the agent vs chatbot issue first. An AI Chatbot handles conversational interactions mimicking human to human conversation – maybe answering questions or providing information usually within a pre-defined domain. You may have come across these as customer service bots, FAQ assistants, or interactive help systems on websites that provide information and respond to your questions. An AI Agent, however, is a different level of capability. It is designed to act autonomously, to take action, to accomplish tasks. There is an element of autonomous decision-making and adaptability within an AI Agent that is directed toward achieving a particular outcome. It can operate without human intervention, actively making choices to meet objectives. AI agents often work within a larger framework or system, interacting with various elements, processing real-time data, and making decisions across different scenarios.
So whilst an AI- Chatbot is primarily conversational, designed to respond to direct input from users in a reactive mode, responding to user prompts, an AI Agent can interact with both users and other systems or other agents. It may engage with external data sources, initiate actions on its own, and make decisions to achieve predefined goals or adapt to changing conditions.
A couple of years ago we interviewed blockchain specialist Anthony Day – we discussed the future of data and the ‘Internet of Things’. Anthony talked about a future world where our devices (from cars to fridges, from phones to door-bell cameras) would be collecting data, and we as owners of this data could set our technology the task of selling it on our behalf by autonomously engaging with different data aggregation platforms, agreeing a price and then transferring data and receiving a payment. We set the objective i.e. maximize revenue, and what we would now call our AI Agent (it wasn’t a common term in 2022 when we conducted the interview) would transact on our behalf. So, the key distinction here is the ability to connect to external systems and take autonomous action.
Optimized for conversational contexts, a ‘simple’ AI Chatbot typically uses natural language processing (NLP) with simpler algorithms or rule-based logic to provide accurate responses, whilst more advanced chatbots may use machine learning models (e.g., transformers like GPT). An AI Agent however uses a combination of machine learning, reinforcement learning, and planning algorithms. It can operate within complex, multi-agent systems and is capable of performing reasoning, learning from the environment, and modifying its behavior based on feedback. It can effectively make decisions, learn from its environment, and adjust its actions to optimize for a given goal without needing constant user input.
The best illustration that we found that neatly explains the functionality of an AI Agent was from David Wiley in his blog entitled … An ‘AI Student Agent’ Takes an Asynchronous Online Course.
“All the technology necessary for an “AI student agent” to autonomously complete a fully asynchronous online course already exists today. I’m not talking about an “unsophisticated” kind of cheating where a student uses ChatGPT to write their history essay. I’m talking about an LLM opening the student’s web browser, logging into Canvas, navigating through the course, checking the course calendar, reading, replying to, and making posts in discussion forums, completing and submitting written assignments, taking quizzes, and doing literally everything fully autonomously – without any intervention from the learner whatsoever”.
So what distinguishes the AI Agent is the ability to take autonomous action and it is this autonomy, allied with adaptability, and their proactive capabilities that make them ideal for handling complex and dynamic challenges in various business settings. We asked ChatGPT for some examples.
Automation of Complex, Multi-Step Processes: AI agents can automate intricate workflows that require sequential decision-making, reducing the need for human intervention in routine or repetitive tasks e.g. Supply Chain Optimization - AI agents can monitor inventory, anticipate demand, coordinate with suppliers, and adjust logistics in real-time to optimize costs and efficiency OR Financial Operations - AI agents can manage invoicing, payment reconciliation, and compliance checks, saving time and reducing errors.
Enhanced Decision-Making: AI agents analyze vast amounts of data in real-time, providing actionable insights and recommendations to decision-makers or acting on decisions autonomously e.g. Dynamic Pricing - AI agents can analyze market trends, competitor pricing, and demand patterns to adjust prices for maximum profitability OR Sales Forecasting - AI agents can identify patterns in sales data, predict trends, and recommend strategies to improve performance.
Proactive Customer Engagement: AI agents can provide highly personalized and proactive customer experiences, improving satisfaction and loyalty e.g. Personalized Marketing - AI agents can analyze customer behavior, preferences, and purchase history to craft tailored marketing campaigns, suggest products, or offer personalized discounts OR Customer Retention - AI agents can predict when customers are at risk of leaving and take proactive steps, such as offering incentives or personalized outreach, to retain them.
Real-Time Adaptability: Businesses operate in dynamic environments where conditions can change rapidly. AI agents excel in adapting to such environments. E.g. Dynamic Resource Allocation - In manufacturing or service industries, AI agents can adjust resource allocation, such as staffing or production schedules, in response to demand fluctuations OR Crisis Management - During disruptions (e.g., supply chain issues, cyberattacks), AI agents can identify the problem, assess possible solutions, and implement contingency plans OR Real-Time Analytics: AI agents continuously monitor key performance indicators (KPIs) and adjust strategies on the fly.
Streamlined Collaboration: AI agents can facilitate collaboration between human teams and across different departments or businesses by acting as intermediaries or coordinators e.g. Project Management - AI agents can track project milestones, allocate tasks, and ensure deadlines are met by keeping all stakeholders informed and aligned.
Cost Reduction and Efficiency: AI agents significantly reduce operational costs by automating repetitive tasks, minimizing errors, and optimizing resource utilization e.g. Workforce Augmentation - By handling mundane tasks, AI agents free up employees to focus on higher-value work, increasing productivity OR Energy Optimization - In industries like manufacturing or data centers, AI agents can optimize energy usage based on demand, reducing operational costs.
Scaling Operations: AI agents enable businesses to scale efficiently without proportional increases in staff or resources e.g. Global Customer Support -AI agents can handle multilingual customer interactions, allowing businesses to expand into new markets without hiring additional personnel OR Automated Sales Funnels - They can guide leads through the sales process autonomously, enabling businesses to manage larger volumes of prospects.
The FT reported that Microsoft, Salesforce and Workday recently put agents at the center of their AI plans, while Google and Meta have also indicated this would be a focus for them when putting their AI models into their products.
“We want to make it possible to interact with AI in all of the ways that you interact with another human being. These more agentic systems are going to become possible, and it is why I think 2025 is gonna be the year that agentic systems finally hit the mainstream”
Kevin Weil, chief product officer, OpenAI
AI agents combine autonomy, adaptability, and proactive problem-solving, which brings them closer to resembling human-like intelligence than previous AI models. Their ability to operate independently and collaboratively while learning from their environment is what makes them so promising for fields requiring continuous optimization and responsiveness to dynamic environments.
In short, AI agents embody the next great advance in AI because they represent a shift from static, tool-like applications to dynamic, interactive “intelligent partners” capable of solving real-world challenges alongside humans. As they continue to evolve, AI agents are likely to play a central role in transforming industries and pushing AI closer to general-purpose intelligence.
[1] OpenAI bets on AI agents becoming mainstream by 2025, Cristina Criddle and George Hammond, October 1 2024, Financial Times