Author: IRPA AI Senior Analyst, Kieran Gilmurray
The business world is experiencing its most significant platform shift since the mobile telephone revolution disrupted entire industries over fifteen years ago. Just as companies that embraced mobile-first strategies captured disproportionate market share, while legacy players scrambled to catch up, we’re now witnessing the emergence of the “agentic economy”.
Almost every company (80%) has incorporated generative AI somewhere into its technology stack. And nearly every one of those companies (80%) says it hasn’t impacted their bottom line. McKinsey calls this the “gen AI paradox”. It is just the usual story of new technology: everyone rushes to adopt it because they’re afraid of being left behind, but they don’t know how to monetise it.
The way out of the paradox is “autonomous agents.” Not just chatbots duct-taped onto a help page, but actual systems embedded in the organizational bloodstream, allowed to do things on their own. That sounds cool in theory, but in practice, it requires the one thing corporations struggle with: thinking clearly about their goals, processes, structures, and incentives before investing money in technology.
So, we’re at another inflection point. Either companies figure out how to deploy agents in a way that’s as transformative as mobile-first was for Apple and Google, or they will end up as the next Blackberry and Nokia, remembered mostly in business school case studies and “Where Are They Now?” slides.
AI Agents vs. Chatbots: Proactive vs. Reactive Systems
Understanding the distinction between AI agents and chatbots is crucial if your goal is to achieve digital transformation in business operations. Chatbots respond to specific queries and, if they are integrated with an LLM-based API, they can then generate unique and relevant content in response to prompts.
AI agents operate with autonomy, memory, and the ability to execute complex workflows across multiple computer systems.
In 2025, an agent can communicate with the customer, confirm that their payment was successful, verify for potential fraud, resolve a customer’s billing issue, reprocess a shipment, and then log all of this information in the CRM.
This shift from reactive to proactive, i.e., from answering to acting, is exactly where competitive advantage now lies.
The leap from chatbots to agents comes from four capabilities:
Memory Systems: Unlike the amnesiac chatbot, an agent remembers the last conversation, the last action, and the last outcome, building a stateful representation of the user interaction or process workflow.
Reasoning Capabilities: Agents can break down complex business challenges into component parts, examine the relationships between those parts, and then create and execute effective strategies to address them.
Integration Power: Like an API cyborg, an agent embeds itself as a nervous system across your company’s software, allowing it to coordinate actions between previously siloed corporate organs such as CRM, ERP, and email.
Autonomous Execution: Perhaps most critically, agents can take action without constant human prompting. This will eventually eliminate the need for many of the middle management’s current tasks, which is a big business advantage.
The Agentic Loop
Agents operate on a four-stage cycle, called the “Agent Loop”. This loop may also be referred to by other names, such as the Perceive-Reason-Act cycle or the Thought-Action-Observation cycle.
Perceive: The agent takes in data from its environment. Not just a user’s text query but a constant stream of information. It could be a real-time feed of market data, usage logs from your CRM, new support tickets, or an urgent email from a key client. Through all this input, the agent builds a high-res map of its assigned domain.
Think: The agent analyzes the data it has perceived, breaks down its high-level goal into executable steps, and decides which tools it needs to use. For instance, an agent would ask to “schedule a meeting with the sales team to discuss the Q3 forecast.” They would first find a mutual opening on everyone’s calendar, then search the shared drive for the relevant forecast document, and finally schedule the meeting with the updated Q3 forecast document attached.
Act: The agent executes the plan by connecting to other systems via APIs. For example, by logging into a CRM, drafting an email, booking a meeting, etc. Here, you have the choice of having a human-in-the-loop to validate and check the agent’s actions or letting it do these things autonomously.
Learn: The agent observes the outcome of its actions and integrates this feedback into its memory, refining its strategies over time. For example, it can learn which outreach messages are most effective and which customer behaviour patterns are the true predictors of churn, among other things.
About the Author: Kieran Gilmurray
Kieran is a globally recognized authority on AI, automation, and digital transformation, having authored multiple influential books and hundreds of articles that have earned him prestigious accolades, including being named a Top 50 Global Thought Leader and Influencer on Generative AI in 2024, a Best LinkedIn Influencer for AI and Marketing, Top 50 Global Thought Leaders and Influencers on Manufacturing 2024, Top 14 people to follow in data and one of the World’s Top 200 Business and Technology Innovators.
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Originally posted on 2026-01-23 in the IRPA AI Network — Announcements & Updates