How Agentic AI will revolutionize business operations – are you ready?

We’re entering an era of autonomous agents capable of executing complex, multistep workflows

News By Rahul Pradhan published 8 November 2024

Generative AI is evolving. Knowledge-based applications like AI chatbots and copilots are giving way to autonomous agents that can reason and perform complex, multistep workflows. These are powered by what is known as agentic AI. This latest development in AI is poised to transform the way businesses operate by being able to understand context, set goals, and adapt actions based on changing conditions.

With these capabilities, agentic AI could perform a whole range of tasks previously thought impossible for a machine to handle – such as identifying sales targets and making pitches, analyzing and optimizing supply chains, or acting as personal assistants to manage employees’ time.

Amazon's recent partnership with Adept, a specialist in agentic AI, signals a growing recognition of the systems’ potential to automate diverse, high complexity use-cases across business functions. But to fully leverage this technology, organizations must first face several challenges with the underlying data – including latency issues, data silos and inconsistent data.

Rahul Pradhan

Rahul Pradhan, VP Product and Strategy, Couchbase.

The three foundations of agentic AI

For its complex functions to operate successfully, agentic AI needs three core components: a plan to work from, large language models (LLMs), and access to robust memory.

A plan allows the agent to execute complex, multi-step tasks. For instance, handling a customer complaint might involve a predefined plan to verify identity, gather details, provide solutions, and confirm resolution.

To follow this plan, an AI agent can use multiple LLMs to break down problems and perform subtasks. In the context of customer services, the agent could call on one LLM to summarize the current conversation with the customer, creating a working memory for the agent to refer to. A second LLM could then plan the next actions, and a third could evaluate the quality of these actions. A fourth LLM could then generate the final response seen by the user, informing them of potential solutions to their problem.

And just like humans, agentic AI systems can’t make informed decisions without using memory. Imagine a healthcare assistant AI with access to a patient's medical history, medical records, and past consultations. Remembering and drawing from this data allows the AI to provide personalized and accurate information, explaining to a patient why a treatment was adjusted or reminding them of test results and doctor's notes.

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