Early chatbots helped companies automate routine interactions, handling FAQs and simple information requests. They saved time but had clear limits like fixed scripts, repetitive dialogues, and no ability to handle long or unstructured queries. Whenever a question fell outside the pre-set scenario, the conversation had to be handed back to a human operator. According to Gartner, by 2020 only 15% of customers preferred talking to chatbots because of these constraints.
The next generation of chatbots, powered by large language models (LLMs), offered more flexibility. They could understand intent in longer messages and respond more naturally, but still lacked control and consistency, sometimes giving irrelevant or inaccurate answers.
That’s where AI agents came in, marking a major shift. Unlike traditional bots, AI agents can autonomously use external tools and systems to complete tasks, hold natural and adaptive conversations, understand long or unstructured queries, and continuously learn from new data to improve their performance.
For businesses, this means handling up to 90% of routine requests without human input, delivering faster and more personalised service, and freeing teams to focus on complex issues. McKinsey
reports that AI agent adoption can reduce service costs by up to 40% and boost customer satisfaction by at least 20%.
At the core of this transformation is the agentic platform — a system that brings together automation, integrations, and governance for managing AI agents at scale. Our enterprise agentic platform takes this further, offering the security, flexibility, and control large organisations need to deploy and oversee AI systems across departments while keeping full visibility and compliance.