Is the Agentic Search Undercurrent an Opportunity for Cloud Providers?

Agentic workloads moving into production continue to grow beyond coding use cases in Artificial Intelligence (AI)- and digital-native enterprises. The adoption of multi-open-weight agentic stacks has placed a greater spotlight on the harnesses being leveraged to manage costs and accuracy in 2026 ever since OpenClaw hit the spotlight earlier this year. As use cases expand beyond code and into more general enterprise workflows, there is a greater focus by the industry to optimize cost per task—which is a combination of both the model’s pricing and the performance of the agent harness. However, there is a competitive layer that can be independent of both—the search Application Programming Interface (API).

In August 2026, Mistral announced its Agentic Search solution as a multi-step retrieval layer to help models search, inspect, and verify information across multi-modal data and documents. Within the library for its Studio and Vibe offerings, it allows enterprises to improve search result accuracy, in turn improving latency and token usage. Back in February 2026, Nebius announced it reached an agreement to acquire Tavily, explicitly stating the move was a way to bring real-time search infrastructure into its AI cloud platform and address a critical capability for agents.

Even leading AI labs have been active in this space. OpenAI had launched its Atlas browser in 2025, and while now deprecated, it laid the foundations for its ChatGPT Work solution. Perplexity’s Computer performs parallel web research, browser automation, and more, while the company also exposes search and agent APIs to developers.

It’s clear that agentic search is the next evolution over a typical one-shot Retrieval Automated Generation (RAG) pipeline. In complex workflows, agents often need to iteratively search, inspect, verify and re-query across multiple sources rather than use a fixed context window. The implication for enterprises is that in the long term, the search layer of their agentic stack will become more critical to improve task accuracy. Each model an enterprise leverages (closed or open) uses its own default search technology, and developers will likely want more control over the tools each model uses for quality control as well as for governance.

The value of the agentic search API is different for various stakeholders. Agent harness players will compete on how well they can match search costs, latency, and capability to a model and task (no matter whether running in the cloud or enterprise edge); model developers will continue to improve their model’s default search capabilities; but cloud providers have an opportunity to capture revenue from the search APIs independent of the model inference costs.

Some of the players in the agentic search space include:

• Parallel: Raised US$100 million in Series B funding in February 2026 at a US$2 billion valuation. Serves customers such as Harvey, Notion, and Opendoor. Announced a partnership with Google Cloud to serve agentic search for Gemini Enterprise Agent Platform in August 2026.
• FireCrawl: Raised US$14.5 million in Series A funding in August 2025. Serves customers such as amotivv, Credal, and Contentful.
• Keenable: Came out of stealth and raised US$26 million in seed funding in August 2026. Struck a partnership with Gradium to support the agentic search capabilities of its voice agent framework Gradbot.
• Exa: Raised US$250 million in Series C funding at a US$2.2 billion valuation. Serves customers such as Cursor, Cognition, HubSpot, and OpenRouter. Announced in August 2026 that Exa will power Firefox’s Smart Window information layer, while already having struck a partnership with Google Cloud in April 2026 for Grounding with Exa Web Search.

For cloud providers, there are three commercial strategies:

1. Develop an in-house agentic search solution.
2. Partner with a specialist.
3. Acquire a specialist.

Hyperscalers are most likely to build a proprietary solution and partner with a specialist. Amazon Web Services (AWS) offers Web Search as a built-in tool within Amazon Bedrock; Google enables multiple agentic search options for its Gemini Enterprise Agent Platform; and Microsoft offers Grounding with Bing Search in Microsoft Foundry. Where customers may look for alternatives options, partnership is the low-risk route. Agentic search is best positioned as a value-add that pulls through model inference and wider platform consumption, rather than direct monetization.

Neoclouds have a different position. Those moving up the stack, like CoreWeave and Crusoe, lack the extensive platform ecosystem hyperscalers can draw on, and partnering with specialists alone would offer little differentiation. Owning the capability is what can allow neoclouds to monetize it directly, akin to Nebius’ Tavily acquisition. However, this would only make sense if acquiring an established specialist with a business pipeline and strong brand to support the neoclouds strategy. Entering this space will not be transformational for neocloud revenue in the near term, but further building out service capabilities is the only way to compete with hyperscalers on this layer of the AI cloud stack. In the short term, it makes sense to monetize the API directly once the company has been acquired, but as the depth and breadth of a neocloud’s services scale, it makes more sense to move it toward value-add.