The data landscape was relatively stable for decades. Relational databases (hello, Oracle!) were the default and dominated, organizing information into familiar columns and rows.
That stability was eroded by successive waves of introduction of NoSQL document stores, graph databases, and more recently vector-based systems. In the age of agentic AI, data infrastructure is once again in flux – and evolving faster than at any point in recent memory.
As 2026 approaches, one lesson has become inescapable: data matters more than ever.
RAG is dead. long live rag
Perhaps the most consequential trend from 2025 that will continue to be debated into 2026 (and perhaps beyond) is the role of RAGs.
The problem is that the basic RAG pipeline architecture is like a basic search. Retrieval finds the result of a specific query at a specific point in time. This is often limited to a single data source, or at least this is how RAG pipelines were constructed in the past (the past was anytime before June 2025).
Those limitations are leading to a growing line of vendors claiming that RAG is dying, on the way, or is already dead,
What are emerging, however, are alternative approaches (such as episodic memory), as well as subtle and improved approaches to RAG. For example, Snowflake recently announced its agentive document analysis The technology, which extends the traditional RAG data pipeline to enable analysis across thousands of sources, without the need for structured data first. A number of other RAG-like approaches are also emerging including graphag Its use and capabilities will likely increase in 2026.
So now RAG is not (completely) dead, at least not yet. Organizations will still find use cases in 2026 where data recovery is required and some advanced version of RAG will likely still fit the bill. Enterprises in 2026 must evaluate use cases individually. Traditional RAG works for static knowledge retrieval, while advanced approaches such as GraphRAG are tailored to complex, multi-source queries.
Episodic memory is table stakes for agentic AI
While RAG will not disappear completely in 2026, one approach that will surpass it in terms of the use of agentic AI is episodic memory, also known as agentic or long-term-context memory. This technology enables LLMs to store and access relevant information over extended periods.
Several systems emerged during 2025 including hindsight, A-MEM framework, General Agentic Memory (GAM)), Langmem, and memobaseRAG will remain useful for static data, but agentic memory is critical for adaptive assistants and agentic AI workflows that must learn from feedback, maintain state, and adapt over time,
In 2026, episodic memory will no longer be a new technology; This will become table stakes for many operational agentic AI deployments.
Use cases for purpose-built vector databases will change
At the beginning of the modern generic AI era, purpose-built vector databases (like Pinecone and Milvus, among others) were all the rage.
For an LLM (generally but not exclusively through RAG) to gain access to new information, it needs access to data. The best way to do this is to encode the data into vectors – that is, a numerical representation of what the data represents.
What became painfully clear in 2025 was that vectors were no longer a specific database type, but a specific data type that could be integrated into existing multimodel databases. So instead of an organization needing to use a purpose-built system, it can simply use an existing database that supports vectors. For example, Oracle supports vectors and so does every database offered by Google.
Oh, and it gets better. Amazon S3, now truly the leader in cloud based object storage Allows users to store vectorsFurther negating the need for a dedicated, unique vector database. This does not mean that object storage replaces vector search engines – performance, indexing and filtering still matter – but it limits the set of use cases where specialized systems are needed.
No, this does not mean that purpose-built vector databases are gone. Like RAG, use cases for purpose-built vector databases will continue in 2026. What will change is that the use cases will be somewhat limited to those organizations that require the highest level of performance or specific customizations that the general purpose solution does not support.
PostgreSQL Ascendant
As 2026 begins, what’s old is new again. The open-source PostgreSQL database will be 40 years old in 2026, yet it will be more relevant than ever.
During 2025, the supremacy of PostgreSQL as the database for building any type of GenAI solution it became clearSnowflake spent $250 million to acquire PostgreSQL database vendor Crunchy Data; databricks spent 1 billion dollars on neon; And SupaBase raised a $100 million Series E, giving it a valuation of $5 billion.
All that money serves as a clear signal that enterprises are defaulting to PostgreSQL. There are several reasons for this including the open-source base, flexibility, and performance. For Vibe coding (a core use case for SupaBase and Neon in particular), PostgreSQL is the standard.
Greater growth and adoption of PostgreSQL is expected in 2026 as more organizations come to the same conclusion as Snowflake and Databricks.
Data researchers will continue to find new ways to solve previously solved problems
It is likely that there will be more innovation to address problems that many organizations already assume: problems that have been solved.
In 2025, we saw many innovations, such as the notion that an AI is capable of parsing data from an unstructured data source such as a PDF. It’s a capability that has been in existence for many years, but has proven harder to operationalize on a larger scale than many anticipated. Databricks now has an advanced parser, and other vendors, including Mistral, have emerged with improvements of their own.
The same is true with natural language to SQL translation. Although some people may have assumed this was a solved problem, it is just that innovation continues to be seen Will see more in 2025 and 2026.
It is important for enterprises to remain vigilant in 2026. Don’t assume that basic capabilities like parsing or natural language are completely solved in SQL. Keep evaluating new approaches that may outperform existing tools.
Acquisitions, investments and consolidation will continue
2025 was a big year for big money to go into data vendors.
Meta invests $14.3 billion in data labeling vendor Scale AI; IBM said it plans to acquire data streaming vendor Confluent for $11 billion; and salesforce Informatica picked up For $8 billion.
Organizations of all sizes should expect the pace of acquisitions to continue in 2026, as large vendors realize the fundamental importance of data to the success of agentic AI.
It is difficult to predict the impact of acquisitions and consolidation on enterprises in 2026. This can eliminate vendor lock-in, and it can also potentially expand platform capabilities.
In 2026, the question won’t be whether enterprises are using AI – it will be whether their data systems are able to keep up with it. As agentic AI matures, sustainable data infrastructure – not clever signals or ephemeral architectures – will determine which deployments scale up and which ones sit quietly.
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