AI Observability in 2026: Why the data layer means everything

If there was ever a year for AI observability, it was 2025. Vendors released assistants to cover a variety of use cases. Coralogix released the first agent (distinct from assistants!), Olly, an autonomous, multi-agent observability platform. The direction of travel is clear, but many vendors and users are about to run into some significant problems with their data layer.

2025: The year of the assistant for AI Observability

The AI Assistant market was valued at around $2.4bn in 2024, with projected growth of up to $47bn. An AI assistant is defined as an AI powered tool that reacts to user prompts, has low autonomy and largely performs simple, well defined tasks. In the context of observability, it’s a tool for converting one query language to another, or executing a specific query or request. 

Assistants are focused on specific outcomes. Coralogix natural language query or platform intelligence capabilities are great examples of assistants. They react to user prompts, and they’re built to solve specific problems, like the conversion of natural language to DataPrime (our proprietary query engine and language). 

These assistants have been a brilliant first step in simplifying the world of observability, and they have proven to the wider market that this type of tooling has a place in our world. In 2025, more than 7 different major observability vendors released AI assistants. While the sentiment around these tools is early, engineers are generally reporting productivity improvements, especially with simpler tasks.

2025: AI-augmented user experiences

Assistants work best when they’re embedded in other interfaces, like a chatbot in the corner of a website. They augment an existing process. Instead of waiting for a support person, one can communicate with an assistant that is trained on the most common problems. This has been the method of choice for every major observability vendor in 2025. 

Each platform has chosen to enrich their existing interface with AI capabilities. This is a sensible first step, because it ensures users are able to easily make use of the tool, without moving to a separate site. This expediency has come at a cost, and this cost betrays the limits of AI-augmented experiences.

What do we lose with an AI-augmented approach to Observability?

We are not shy about our beliefs about observability data. Coralogix has demonstrated continued value for over 4,000 customers, driven by our architecture and approach to data management and ownership. We presented on stage at AWS re:Invent and discussed this very topic in December. This is one of the ways that organizations can realize a better return on their investment in observability. By pulling out business impacting insights (for example, consider a simple error log vs an enriched document that describes the cost of an error over the past 24 hours), the true value of this data becomes much more quantifiable. 

In order for data of any type in any organization to be utilized, users must have effective access to it. Users of all backgrounds need to be able to access this data meaningfully. This is what we lose with an AI-augmented workflow. It does not matter if a natural language query is present somewhere in your log analytics screen – a log analytics screen is not a natural interface for a marketing professional. By adding AI, we bolster the users we already have, but we don’t make things any more accessible. How do we solve this problem?

2026: Autonomous observability

The trend is clear – we’re moving from assistants to agents, across the board. The impact to observability is significant, because agentic experiences differ fundamentally from assistants in a number of ways.

Augmented AND new workflows

Agentic experiences open up the potential for entirely new workflows. Rather than diving into the same UI, entirely separate UIs that are centered around an AI experience become possible. This is because your agent is able to perform the actions that you need, on your behalf. It no longer leans on the rest of the traditional interface to fill the gaps in functionality.

Problem, not instruction

Agents (especially multi-agent platforms like Olly) are able to investigate problems. They are not restricted to small, simple tasks. Olly shows each query, each “thought”, as it solves a problem. As a result, the user can see the steps Olly has taken to come to a conclusion.

This jump from instruction to problem statement, indicates a higher level of abstraction. Not only does this higher abstraction mean that the agent can handle more of the heavy lifting, but it also makes agentic tools more accessible to non-technical users. 

Decision and delivery

Agents can reach into downstream tools and make choices. For example, Olly is able to initially decide which data sources are most appropriate for a given problem. It may choose to analyze triggering alarms, and correlate them with error logs from the same service. This decision layer (referred to as orchestration) gives Olly the ability to decide how it will approach a problem. Agents are able to affect downstream systems and actually drive outcomes. This distinguishes them from simpler systems that do not have these integrations.

Yet, with all of these ambitions and goals, a new constraint appears. Goldratt’s theory of constraints argues that in a linear system, only one constraint can act at one time. As we lift one constraint (limited access to data due to complex interfaces), a new one will reveal itself. Indeed, that constraint is already revealing itself. The access and quality of the underlying dataset. 

The coming bottleneck: the data layer

Companies reported a near 30% increase in data-management spending in 2022 alone, with the 2024 market cap of data management software (not including DBMS solutions) at $13.5b. Many vendors rushed to ship an assistant in 2025 without investing in the underlying query language or data architecture that would allow a truly autonomous system to operate. Most assistants rely on simple retrieval and narrow queries, but agents cannot. They depend on fast, expressive, and highly composable access to data in order to “reason,” correlate, and decide. 

This gap becomes painfully clear when an agent tries to investigate a complex issue, only to find that the data platform can’t answer its questions quickly enough, or at all. Without a powerful, expressive language for cross-domain querying, an agent is effectively trapped in the same shallow workflows as an assistant. In 2026, the organizations that thrive will be those that recognize that autonomy doesn’t start with the UI. It starts with the data. 

How do we fix this constraint?

At the base level, any platform must be able to store enormous volumes of data at a cost profile that remains sustainable. More than that, agents will depend on an expressive language for accessing data, and the more tailored that language is to the problem domain, the better the performance will be.

Cleaning and enriching data automatically

Cheap storage alone won’t cut it. The data itself must be clean. That means automated parsing, enrichment, and normalization. All the steps required to turn raw events into structured, reliable information. An agent will be able to decipher messy logs and spans, or poorly labeled metrics, but it will require more data to make effective decisions, which strains context windows and invites hallucinations.

Organizing data into higher-order structures

The data must be organized. Not as loosely connected tables, but through coherent, higher-order constructs like Datasets in the Coralogix DataPrime engine. These structures provide agents with a semantic map of the environment: what data exists, how it relates, and how it can be combined to solve complex problems that span data types and services. Without this organizational layer, agents are querying the entire pool of telemetry, and will bring back more data than they need. 

Is Coralogix ready for the challenges of 2026?

With the release of Olly, Coralogix has already made the jump from assistant to multi-agent platform. With agents for alerting, dashboards, logs, metrics, traces, and more, Olly has the analytical capabilities to solve a plethora of observability and security challenges. The difference between us and the rest of our competitors is our early focus on the data layer. 

We released the Coralogix DataPrime query engine in 2021 for one reason – observability is, at its core, a data problem. It requires accessing large volumes of heterogeneous data in seconds, with complex transformations and joins, all within a reasonable cost parameter. We knew that investing in not only a powerful query layer, but a fully featured data pipeline would continue to pay dividends.

While many of our competitors are figuring out how they are going to scale with AI volumes in 2026, our platform has been ready for this task for years. In 2026, the evidence is clear: the constraint is more than models, it’s the underlying data layer. We have been preparing for this event for years, and now we’re ready to scale like never before.