An LLM agent architecture is a framework combining a large language model with other components to enable better task execution and real-world interaction.
What role do agents play in an LLM architecture?
LLM agents are advanced AI assistants that leverage large language models to perform complex tasks systematically and autonomously. They’re designed not just to generate text, but also to manage multi-step assignments through a combination of planning, acting, and learning from feedback. Unlike a basic LLM, which follows defined instructions or workflows, an LLM agent can decide which tools or data sources to use based on the task at hand and can proactively adapt its approach to solving a problem on the fly.
For example, if you ask an LLM agent to plan a vacation, it can break down the task into (1) looking up weather forecasts, (2) checking travel options within your budget, (3) suggesting itineraries, and (4) refining its recommendations based on your feedback. The source of this flexibility is LLM function calling, which includes web searches or calculations, and the ability to apply different tools dynamically.
An LLM agent brings much needed flexibility to AI, enabling it to handle more complex applications, like diagnosing IT issues or coordinating multi-step tasks in your company. In the future, it’s expected to gain even greater capabilities in areas like memory improvement, enhanced context management, and self-criticism.
Like the LLM itself, an LLM agent architecture, plays a key role in generative AI frameworks, such as Retrieval-Augmented Generation (RAG). Active retrieval-augmented generation injects your LLM with structured and unstructured enterprise data, resulting in more accurate responses and fewer AI hallucinations.
What are the LLM agent architecture components?
An LLM agent architecture consists of several components, all of which work together to complete complex tasks by adapting to various scenarios:
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Brain
At the core of the agent is the brain, which handles major decision-making and coordinates with the other components. It defines the agent’s goals, the tools at its disposal, and the planning strategies it can use – basically creating a structure for how the agent should behave. The brain can also include a persona that guides the agent’s style and tools preferences in each interaction.
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Memory
The memory component keeps track of the agent’s interactions and is split into short-term and long-term memory. Short-term memory logs an agent’s immediate chain-of-thought reasoning for single queries. Long-term memory stores histories of interactions that can span weeks or months. Memory retrieval, which is based on the relevance to the user’s query, enables the agent to provide more contextually aware responses.
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Tools
Tools are specialized APIs or workflows (for example, a code interpreter or a search API) that the agent calls on for specific tasks. The agent’s tools give it the versatility to handle requests that require additional functions beyond simple text responses.
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Planning
The planning component breaks down complex questions into manageable parts. It also includes reflection techniques that help the agent review and improve its response plan, by enhancing accuracy and relevance.
LLM agent architecture applications
LLM agents can be used in a wide variety of applications from transforming how you interact with data, through personalizing your users’ experiences, to optimizing your workflows. 5 key types of data agents are listed below.
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Talk to your data agents
Talk to your data agents allow users to query complex datasets using RAG conversational AI tools, helping them solve complex questions that standard tools just can’t handle. It breaks down multifaceted questions by analyzing documents, tables, and fragmented data, making it ideal for analytics and research in fields like financial services and healthcare.
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Swarm of agents
Like an array of specialized microservices, a swarm of agents can collaborate to solve larger tasks in a decentralized manner. For example, software development teams can work together to build applications, or simulate business environments, for marketing, finance, or customer care.
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Recommendation and experience design agents
In e-commerce and customer service, recommendation and experience design agents achieve AI personalization by conversationally guiding users through products, suggesting relevant items, or offering concierge-level assistance, thus enhancing the shopping or browsing experience.
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Customized AI author agents
Tailored AI author agents help generate content for specific audiences, like customers, partners, investors or employees. They do this by drawing on previous work and adapting the tone to the task.
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Multi-modal agents
Multi-modal agents process diverse inputs like text, images, video, and audio, to deliver more data-driven insights. With applications ranging from analyzing documents to enhancing presentations, the multi-modal agent approach is valuable for complex data handling across industries.
LLM agent architecture with GenAI Data Fusion
GenAI Data Fusion, the RAG tools developed by K2view, enhances LLM agent architecture by integrating advanced prompt engineering techniques within the RAG architecture. Perfect for use cases with complex interactions, GenAI Data Fusion leverages chain-of-thought prompting within its LLM agent architecture to produce more compliant, complete, and current outputs, and offers:
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Instant access to data, significantly enhancing the LLM agent’s responses.
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Advanced data security, dynamically masking sensitive PII (Personally Identifiable Information).
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Real-time recommendations, by handling data service access requests and providing intelligent insights.
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Multi-source data aggregation, across enterprise systems, via API, CDC, messaging, or streaming.
The LLM agent architecture in GenAI Data Fusion also contributes to grounding data within the model, for more accurate and relevant responses across a wide range of generative AI use cases.
Discover AI Data Fusion, the only suite of RAG
tools with an LLM agent architecture at its core.
