For decades the data landscape has been relatively static. Relational databases (hello, Oracle!) are the default and dominate, organizing information into familiar columns and rows.

That stability has eroded as successive waves have introduced NoSQL document stores, graph databases, and more recently vector-based systems. In the age of agent AI, the data infrastructure is once again changing – and evolving faster than at any point in recent memory.

As 2026 dawns, one lesson becomes inevitable: data is more important than ever.

RAG is dead. Long live RAG

Perhaps the most consequential trend in 2025 that will continue to be debated into 2026 (and perhaps beyond) is the role of RAG.

The problem is that the original RAG pipeline architecture is like a basic search. Retrieval finds the result of a specific query, at a specific point in time. It’s also often limited to a single data source, or at least that’s how RAG pipelines have been built in the past (the past is anytime before June 2025).

The limitations lead to a growing conga line of vendors claiming that RAG is dying, on the way out, or it’s dead.

What has emerged, however, are alternative approaches (such as contextual memory), as well as nuanced and improved approaches to RAG. For example, Snowflake recently announced this agent document analysis technology, which expands the traditional RAG data pipeline to enable the analysis of thousands of sources, without the need to have structured data first. There are also many other RAG-like methods emerging including GraphRAG which will likely only grow in use and capability by 2026.

So now RAG is not (completely) dead, at least not yet. Organizations will still find use cases in 2026 where data capture is needed and some enhanced version of RAG will likely fit the bill. Businesses in 2026 will need to evaluate use cases individually. Traditional RAG works for static knowledge extraction, while advanced methods like GraphRAG suit complex, multi-source queries.

Context memory is the stakes on the table for agent AI

While RAG will not completely disappear by 2026, one method that will likely surpass it in terms of use for agent AI is context memory, also known as agent memory or long-term context. This technology enables LLMs to store and access important information for long periods of time.

Many such systems are emerging in the course of 2025 including Hindsight, A-MEM structure, General Agent Memory (GAM), LangMem, and Memobase. RAG remains useful for static data, but agent memory is critical for adaptive assistants and agent AI workflows that must learn from feedback, maintain state, and adapt over time.

In 2026, contextual memory will no longer be a novel technique; these will be table stakes for many operational agentic AI deployments.

The use cases of purpose-built vector databases will change

At the beginning of the modern generative AI era, purpose-built vector databases (such as Pinecone and Milvus, etc.) were all the rage.

In order for an LLM (generally but not exclusively through RAG) to gain access to new information, it must be able to access the data. The best way to do that is to encode the data in vectors – that is, a numerical representation of what the data represents.

In 2025 what is becoming increasingly clear is that vectors are no longer a specific database type but a specific data type that can be integrated into an existing multimodel database. So instead of an organization having to use a purpose-built system, it can simply use an existing database of support vectors. For example, Oracle supports vectors and so does every database offered by Google.

Oh, and it’s better. Amazon S3, long the de facto leader in cloud based object storage, today allows users to store vectorsfurther negating the need for a dedicated, unique vector database. That doesn’t mean that object storage will replace vector search engines – performance, indexing, and filtering are still important – but it does reduce the set of use cases where specialized systems are needed.

No, that doesn’t mean purpose-built vector databases are dead. Like RAG, there will continue to be use cases for vector databases built for the purpose of 2026. What will change is that the use cases are likely to be small for organizations that require the highest level of performance or a specific optimization that is not supported by a general purpose solution.

PostgreSQL ascendant

As 2026 begins, what’s old is new again. The open-source PostgreSQL database will be 40 years old in 2026, but it will be more relevant than ever.

By 2025, PostgreSQL’s supremacy will be the go-to database for building any kind of GenAI solution. became apparent. Snowflake spent $250 million to acquire PostgreSQL database vendor Crunchy Data; Databricks spent $1 billion in Neon; and Supabase raised a $100 million series E giving it a $5 billion valuation.

All the money serves as a clear signal that businesses are defaulting to PostgreSQL. There are many reasons including open-source base, flexibility, and performance. For vibe coding (a core use case for Supabase and Neon in particular), PostgreSQL is the standard.

Expect to see more growth and adoption of PostgreSQL in 2026 as more organizations come to the same conclusion as Snowflake and Databricks.

Data scientists will continue to find new ways to solve problems that have already been solved.

There is likely to be more innovation to help solve problems that many organizations probably think: problems have been solved.

In 2025, we will see many innovations, such as the idea that an AI will be able to parse data from an unstructured data source like a PDF. That’s a capability that’s been around for years, but has proven more difficult to operationalize at scale than many believed. Databricks now has an advanced parser, and other vendors, including Mistral, have emerged with their own improvements.

The same is true of the natural language interpretation of SQL. While some may believe this is a solved problem, it is continues to see innovation in 2025 and see more in 2026.

It is important for businesses to remain vigilant in 2026. Don’t consider foundational capabilities such as parsing or natural SQL language to be fully resolved. Continue to evaluate new methods that may be better than existing tools.

Acquisitions, investments, and consolidation will continue

2025 is a big year for big money going to data vendors.

Meta invested $14.3 billion to data labeling vendor Scale AI; IBM said it plans to acquire data streaming vendor Confluent for $11 billion; and Salesforce took Informatica for $8 billion.

Organizations should expect the adoption of all sizes to continue through 2026, as major vendors realize the fundamental importance of data to the success of agent AI.

The impact of acquisitions and consolidation of businesses in 2026 is difficult to predict. This can lead to vendor lock-in, and it can also lead to expanded platform capabilities.

In 2026, the question won’t be whether businesses use AI – it’s whether their data systems can keep up with it. As agent AI matures, strong data infrastructure — not smart prompts or short-sighted architectures — will determine what scale is deployed and where it stagnates.

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