Generative AI is evolving. Knowledge-based applications such as chatbots and AI co-pilots are giving way to autonomous agents that can reason and perform complex, multi-step workflows. These work with what is known as agent AI. This latest advancement 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, AI agents could perform a wide range of tasks previously considered impossible for a machine to perform, such as identifying sales targets and submitting proposals, analyzing and optimizing supply chains, or acting as personal assistants to manage lead time. the employees.
Amazon's recent partnership with Adept, a specialist in agent AI, indicates a growing recognition of the systems' potential to automate diverse and highly complex use cases across business functions. But to fully take advantage of this technology, organizations must first address several challenges with the underlying data, including latency issues, data silos, and inconsistent data.
Rahul Pradhan, VP Product & Strategy, Couchbase.
The three foundations of agent AI
For its complex functions to work successfully, agent 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 example, 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 analyze problems and perform subtasks. In the context of customer services, the agent could use an LLM to summarize the current conversation with the customer, creating a working memory that the agent can refer to. A second LLM could plan the next actions and a third could evaluate the quality of these actions. A fourth LLM could generate the final answer seen by the user, informing them of possible solutions to their problem.
And just like humans, agent AI systems cannot 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 leveraging this data allows AI to provide personalized and accurate information, explaining to the patient why a treatment was adjusted or reminding them of test results and doctor's notes.
Both short-term and long-term memory are needed for tasks that require immediate attention and to develop an understanding of context that the AI can rely on for future inferences. But herein lies one of the main barriers preventing the optimization of agent AI today: often, enterprise databases are not advanced enough to support these memory systems, limiting the potential of AI. to offer accurate and personalized information.
The data architecture needed to support AI agents
The predominant approach to meeting memory system requirements is the use of stand-alone, special-purpose database management systems for various data workflows. However, the practice of using a complex network of these independent databases can hurt an AI's performance in several ways.
Latency issues arise when each of the different databases used have different response times, causing delays that can disrupt AI operations. Additionally, data silos, where information is isolated in separate databases, prevent AI from having a unified view and make comprehensive analysis difficult, causing the agent to lose connections and provide incomplete results. And at a more fundamental level, inconsistent data (due to variations in quality, format, or accuracy) can also cause errors and biased analysis, leading to flawed decision-making. Using multiple single-purpose database solutions also creates data dispersion, complexity, and risk, making it difficult to trace the source of AI hallucinations and purge incorrect variables.
Many databases are also not suitable for the speed and scalability that AI systems require. Its limitations become more pronounced in multi-agent environments, where rapid access to large volumes of data (e.g., via LLM) is essential. In fact, only 25% of companies have high-performance databases capable of managing unstructured data at high speed, and only 31% have consolidated their database architecture into a unified model. These databases will struggle to meet the demands of GenAI, let alone support any form of unlimited AI growth.
As GenAI evolves and agent AI becomes more prevalent, unified data platforms will be critical to any successful AI implementation by organizations. Updated data architectures deliver benefits by reducing latency with cutting-edge technology, efficiently managing structured and unstructured data, optimizing access, and scaling on demand. This will be a key advancement in building cohesive, interoperable and resilient memory infrastructures and will allow enterprises to finally capitalize on the automation, precision and adaptability that agent AI has to offer.
Embracing the AI revolution
Agent AI ushers in a new era where AI agents act as collaborators and innovators, fundamentally changing the way humans interact with technology. Once companies have overcome the challenges associated with disparate data sources and optimized memory systems, they will unlock the widespread use of tools that can think and learn like humans, with unprecedented levels of efficiency, insight, and automation.
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