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, 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
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