Generative AI is advancing beyond knowledge-based applications such as chatbots and copilots, evolving into autonomous AI agents capable of reasoning and handling complex, multistep workflows. These agentic AI systems can automate diverse, high-complexity use cases across various industries and business functions.
This article explores the different types of memory systems required for agentic AI, identifies the key challenges these systems face, and discusses how to integrate disparate databases into a cohesive memory system.
What is agentic AI?
Agentic AI marks a new era where AI agents act as collaborators and innovators, fundamentally changing how humans interact with technology. It refers to systems designed to autonomously pursue complex goals with minimal human supervision, demonstrating decision-making, planning, and adaptive execution to complete multi-step processes. These AI agents aim to operate more like humans — understanding context, setting goals, reasoning through tasks, and adapting their actions based on changing conditions.
Agentic AI requires three critical components: large language models (LLMs), memory, and a plan. Each component plays a distinct role, and when integrated, they enable the agent to achieve more than the sum of its parts.
- LLMs: An agent may use LLMs multiple times to break down problems and perform subtasks. It might begin by calling an LLM to summarise the current conversation, creating working memory. Another LLM could then plan potential next steps, followed by a third LLM to assess the quality of each option. Finally, a fourth LLM call generates the final response for the user. This approach — using specialised LLM calls for different purposes — allows agents to achieve significantly better performance than relying on a single LLM call.
- Memory: Long-term memory allows agents to answer questions or solve problems more effectively. For example, a retailer’s AI agent could access detailed information about products, warranty policies, and company history, enabling more accurate responses to customer inquiries.
- Plan: To reliably carry out complex, multi-step tasks, an agent needs a repository of “plans” or workflows. These plans contain procedural knowledge, guiding the agent through each necessary step and ensuring tasks are completed successfully and in the correct order.
Types of memory systems in agentic AI
Memory systems are fundamental for AI agents to function effectively. These systems enhance an agent’s functionality, efficiency, and ability to carry out complex tasks by storing and retrieving information, maintaining context, learning from past experiences, and making informed decisions. A robust memory system is crucial for organising and storing various types of information that AI agents can retrieve during inference, ensuring accurate and efficient performance.
The memory system for AI agents can be classified into short-term and long-term memory systems. Short-term memory includes the working memory that temporarily holds information being actively processed. This is essential for tasks that require immediate attention and manipulation. Another function of working or short-term memory is context management, which is vital for AI agents to maintain the context of ongoing tasks. This ability is necessary for coherent operations and decision-making, enabling agents to process complex instructions across multiple interactions and workflows.
The second type of memory system for AI agents is long-term memory, which encompasses episodic, semantic, and procedural memory.
- Episodic memory stores specific events and experiences, allowing the AI to recall past interactions and use that knowledge to inform future decisions. This is critical for learning and adaptation.
- Semantic memory contains general knowledge about the world, including facts and concepts, helping the AI understand and reason about encountered information for more accurate responses.
- Procedural memory stores knowledge of how to perform tasks, enabling the AI to execute learned procedures automatically — similar to how humans remember how to ride a bike or type on a keyboard. This component helps maintain the flow and context of interactions with users, ensuring the AI delivers coherent and contextually appropriate responses throughout the course of a conversation.
Challenges with existing approaches
The prevalent approach to meeting memory system requirements involves using specialised, standalone database management systems for various data workflows or types:
- In-memory databases are used for caching and providing quick access to frequently needed data.
- Relational and non-relational databases handle operational and transactional data.
- Data warehouses and OLAP systems manage historical data sets and support complex queries.
- Vector databases manage vectors, which are essential for tasks involving embeddings and similarity searches.
However, relying on a complex web of standalone databases can negatively impact an AI agent’s performance.
- Latency issues: Different databases have varying response times, leading to inefficiencies.
- Data silos: Disparate databases make comprehensive data analysis difficult.
- Inconsistent data: Variations in data consistency and integrity can result in errors.
Integrating these disparate databases into a cohesive, interoperable, and resilient memory system for AI agents presents its own challenges. Many commonly used database services are not optimised for the speed and scalability required by AI agent systems, and their individual weaknesses become more pronounced in multi-agent environments.
As AI agents evolve, the integration of memory systems becomes increasingly critical. Overcoming the challenges of disparate data sources and optimising memory systems for speed and scalability will be essential to realising the full potential of autonomous AI agents. By adopting unified data platforms that support high performance and low latency, businesses can fully harness the power of agentic AI to drive innovation, improve efficiency, and achieve strategic goals. The future of AI lies in creating systems that not only think and learn like humans but also seamlessly integrate diverse data to provide intelligent, adaptive, and context-aware responses autonomously.