For many years the info panorama was comparatively static. Relational databases (good day, Oracle!) had been the default and dominated, organizing data into acquainted columns and rows.
That stability eroded as successive waves launched NoSQL doc shops, graph databases, and most not too long ago vector-based methods. Within the period of agentic AI, knowledge infrastructure is as soon as once more in flux — and evolving quicker than at any level in current reminiscence.
As 2026 dawns, one lesson has develop into unavoidable: knowledge issues greater than ever.
RAG is lifeless. Lengthy dwell RAG
Maybe essentially the most consequential pattern out of 2025 that can proceed to be debated into 2026 (and perhaps past) is the position of RAG.
The issue is that the unique RAG pipeline structure is very like a primary search. The retrieval finds the results of a selected question, at a selected cut-off date. It is usually typically restricted to a single knowledge supply, or no less than that's the way in which RAG pipelines had been constructed up to now (the previous being anytime previous to June 2025).
These limitations have led a rising conga line of distributors all claiming that RAG is dying, on the way in which out, or already lifeless.
What’s rising, although, are different approaches (like contextual reminiscence), in addition to nuanced and improved approaches to RAG. For instance, Snowflake not too long ago introduced its agentic doc analytics expertise, which expands the normal RAG knowledge pipeline to allow evaluation throughout 1000’s of sources, without having to have structured knowledge first. There are additionally quite a few different RAG-like approaches which might be rising together with GraphRAG that can seemingly solely develop in utilization and capabilities in 2026.
So now RAG isn't (fully) lifeless, no less than not but. Organizations will nonetheless discover use instances in 2026 the place knowledge retrieval is required and a few enhanced model of RAG will seemingly nonetheless match the invoice.
Enterprises in 2026 ought to consider use instances individually. Conventional RAG works for static data retrieval, whereas enhanced approaches like GraphRAG go well with advanced, multi-source queries.
Contextual reminiscence is desk stakes for agentic AI
Whereas RAG gained't fully disappear in 2026, one strategy that can seemingly surpass it by way of utilization for agentic AI is contextual reminiscence, often known as agentic or long-context reminiscence. This expertise permits LLMs to retailer and entry pertinent data over prolonged intervals.
A number of such methods emerged over the course of 2025 together with Hindsight, A-MEM framework, Common Agentic Reminiscence (GAM), LangMem, and Memobase.
RAG will stay helpful for static knowledge, however agentic reminiscence is vital for adaptive assistants and agentic AI workflows that should study from suggestions, keep state, and adapt over time.
In 2026, contextual reminiscence will now not be a novel method; it is going to develop into desk stakes for a lot of operational agentic AI deployments.
Goal-built vector databases use instances will change
Firstly of the fashionable generative AI period, purpose-built vector databases (like Pinecone and Milvus, amongst others) had been all the trend.
To ensure that an LLM (usually however not solely by way of RAG) to get entry to new data, it must entry knowledge. One of the simplest ways to try this is by encoding the info in vectors — that’s, a numerical illustration of what the info represents.
In 2025 what grew to become painfully apparent was that vectors had been now not a selected database sort however quite a selected knowledge sort that may very well be built-in into an current multimodel database. So as a substitute of a corporation being required to make use of a purpose-built system, it might simply use an current database that helps vectors. For instance, Oracle helps vectors and so does each database supplied by Google.
Oh, and it will get higher. Amazon S3, lengthy the de facto chief in cloud based mostly object storage, now permits customers to retailer vectors, additional negating the necessity for a devoted, distinctive vector database. That doesn’t imply object storage replaces vector search engines like google — efficiency, indexing, and filtering nonetheless matter — but it surely does slim the set of use instances the place specialised methods are required.
No, that doesn't imply purpose-built vector databases are lifeless. Very like with RAG, there’ll proceed to be use instances for purpose-built vector databases in 2026. What is going to change is that use instances will seemingly slim considerably for organizations that want the very best ranges of efficiency or a selected optimization {that a} general-purpose answer doesn't help.
PostgreSQL ascendant
As 2026 begins, what's previous is new once more. The open-source PostgreSQL database might be 40 years previous in 2026, but will probably be extra related than it has ever been earlier than.
Over the course of 2025, the supremacy of PostgreSQL because the go-to database for constructing any sort of GenAI answer grew to become obvious. Snowflake spent $250 million to accumulate PostgreSQL database vendor Crunchy Information; Databricks spent $1 billion on Neon; and Supabase raised a $100 million sequence E giving it a $5 billion valuation.
All that cash serves as a transparent sign that enterprises are defaulting to PostgreSQL. The explanations are many together with the open-source base, flexibility, and efficiency. For vibe coding (a core use case for Supabase and Neon specifically), PostgreSQL is the usual.
Anticipate to see extra progress and adoption of PostgreSQL in 2026 as extra organizations come to the identical conclusions as Snowflake and Databricks.
Information researchers will proceed to search out new methods to unravel already solved issues
It's seemingly that there might be extra innovation to assist issues that many organizations seemingly assume are already: solved issues.
In 2025, we noticed quite a few improvements, just like the notion that an AI is ready to parse knowledge from an unstructured knowledge supply like a PDF. That's a functionality that has existed for a number of years, however proved tougher to operationalize at scale than many assumed. Databricks now has a sophisticated parser, and different distributors, together with Mistral, have emerged with their very own enhancements.
The identical is true with pure language to SQL translation. Whereas some may need assumed that was a solved downside, it's one which continued to see innovation in 2025 and can see extra in 2026.
It's vital for enterprises to remain vigilant in 2026. Don't assume foundational capabilities like parsing or pure language to SQL are totally solved. Hold evaluating new approaches that will considerably outperform current instruments.
Acquisitions, investments, and consolidation will proceed
2025 was a giant 12 months for giant cash going into knowledge distributors.
Meta invested $14.3 billion in knowledge labeling vendor Scale AI; IBM stated it plans to accumulate knowledge streaming vendor Confluent for $11 billion; and Salesforce picked up Informatica for $8 billion.
Organizations ought to anticipate the tempo of acquisitions of all sizes to proceed in 2026, as large distributors notice the foundational significance of knowledge to the success of agentic AI.
The influence of acquisitions and consolidation on enterprises in 2026 is difficult to foretell. It could result in vendor lock-in, and it will probably additionally probably result in expanded platform capabilities.
In 2026, the query gained’t be whether or not enterprises are utilizing AI — will probably be whether or not their knowledge methods are able to sustaining it. As agentic AI matures, sturdy knowledge infrastructure — not intelligent prompts or short-lived architectures — will decide which deployments scale and which quietly stall out.
