Exa is building a search engine for AI agents. The user is a model trying to complete a task with accurate, current, and structured information.

Human search is shaped by short queries, clicks, page rankings, ads, and search engine optimization. Agentic search works differently. An AI agent may ask long, messy, multi step questions. It may run dozens of searches in a single workflow. It may need source passages, full page content, structured company data, code examples, citations, or summaries that can be passed directly into a language model. For that world, traditional search architecture starts to look like the wrong tool.

Exa’s central claim is simple: AI agents need search that is designed from the ground up for them. The company describes its product as one API for search, crawling, and research agents. Its own product page frames the goal as organizing the web for agents, with search that can range from low latency results to deeper research with grounded citations and structured outputs.

Why AI agents need a different kind of search

A person searching the web can tolerate friction. You can skim a result page, open tabs, judge credibility, rewrite the query, and combine information manually. An AI agent does not work that way. It needs a retrieval layer that gives it the right evidence at the right moment, in a format the model can use.

This changes the requirements for search in several ways.

  • Queries are longer and more complex. Agents often ask for relationships, constraints, comparisons, and task specific context rather than simple keywords.
  • Search volume is higher. One user request can trigger many tool calls, and search may be only one step in a larger workflow.
  • Latency compounds quickly. If an agent searches, reasons, searches again, extracts data, and verifies results, every delay affects the whole task.
  • Raw links are not enough. Agents need relevant passages, full text, structured fields, summaries, and citations.
  • Search quality directly affects output quality. A strong model with weak retrieval still hallucinates or misses important context.

This is why Exa talks about the bottleneck in AI shifting from intelligence to information. Modern models can reason impressively, but they are only useful in real workflows when they can access current and reliable knowledge. Retrieval is becoming part of the reasoning system, not a side feature.

What Exa actually offers

Exa’s product is built around an API that gives developers access to search, crawling, content extraction, research outputs, and structured data. The company’s product page emphasizes that it is designed to make agents smarter by giving them comprehensive coverage across search verticals and usable context for solving problems.

The most important word here is usable. A search result for an AI agent is not just a ranked URL. It may be a concise excerpt, a structured object, a grounded answer, or a filtered set of pages from selected domains. That makes Exa closer to an information infrastructure layer than a normal search interface.

Search results for agent workflows

Exa can return standard search results such as URLs and page contents, but it also provides key passages extracted directly from source pages. Its product materials describe highlights as token efficient excerpts designed to pass into language models. Exa says up to 25 trillion tokens passed to models each week are concise excerpts powered by these highlights.

That matters because full web pages are often noisy. They contain navigation, repeated text, ads, unrelated sections, and long passages that are irrelevant to the task. If an agent has to process all of that, costs rise and quality can fall. A strong extraction layer can reduce the number of tokens sent to a model while preserving the evidence needed to answer well.

Structured outputs for research agents

Exa also presents structured outputs as a core part of its search product. Its product page says it can extract enrichments as structured outputs across more than 70 million companies. For agents that research markets, vendors, hiring signals, product categories, or sales accounts, this kind of output is more useful than a list of pages.

Consider a research agent that needs to identify software companies with a specific integration, a recent funding event, and open roles in a technical function. A human might search, read, copy data into a spreadsheet, and check sources. An agent needs a retrieval system that can gather and normalize that data in a repeatable way. That is the kind of workflow Exa is positioning itself to support.

Search modes for speed and depth

Exa’s search materials describe different latency and quality modes. The company highlights Exa Instant as returning results in under 180 milliseconds. It also describes deeper agentic research that can take several seconds when the task requires more comprehensive retrieval.

Not every agent task needs the same search profile. A chat assistant may need fast grounding for a simple factual question. A coding agent may need precise documentation snippets. A go to market research agent may need broad coverage across company websites, profiles, news, and databases. Search for agents is not one setting. It is a set of tradeoffs between speed, recall, precision, cost, and output format.

How Exa differs from traditional search engines

Traditional search engines were built for human attention. Their ranking systems use signals that make sense for people, including clicks, links, behavior, freshness, location, and page level authority. That works well for many consumer searches, but it is less natural for agents.

An AI agent does not care which snippet is most clickable. It cares whether the information is complete, correct, relevant, and machine usable. It may prefer a dense technical document over a popular summary. It may need an obscure source that never ranks well for consumer traffic. It may need to search code repositories, documentation, research papers, company pages, and product listings in a single workflow.

Exa argues that many products marketed as AI search are wrappers over existing search engines. A wrapper can be useful for basic retrieval. It can take a normal search result, summarize it, and pass it to a model. But if the underlying engine was optimized for human queries and click behavior, the agent inherits those constraints.

Exa’s claim is that it has taken the harder path by building search infrastructure directly for AI use. Its company materials describe crawlers that track hundreds of billions of URLs, custom retrieval models, embedding models trained on its own GPU cluster, and vector databases designed for the high query volume that agents require.

The infrastructure behind search for AI agents

Search looks simple from the outside because the interface is simple. Underneath, it is one of the hardest systems to build. A search engine must crawl the web, remove duplicates, process pages, detect changes, create indexes, rank results, handle spam, serve queries quickly, and continuously improve relevance.

For AI agents, the infrastructure burden grows. The system must understand semantic intent rather than keyword overlap. It must retrieve useful passages for long questions. It must serve high query volume because agents may perform many searches per task. It must also support content extraction, structured outputs, and citations.

Exa’s Series C announcement states that its crawlers track over 500 billion URLs and that the company has built new vector databases for the extremely high queries per second that agents need. The same announcement says Exa plans to scale infrastructure to handle hundreds of thousands of searches per second.

Latency is central to Exa’s positioning. SiliconANGLE reported that Exa’s search engine could run more than 100 queries per second with latency under 450 milliseconds at the time of a previous funding announcement. Exa’s more recent product page claims its Instant search returns results under 180 milliseconds. The exact number depends on mode and context, but the direction is clear: agent search has to be fast enough to sit inside workflows, not beside them.

Semantic search and retrieval quality

One of Exa’s key technical ideas is semantic search. Instead of matching only keywords, semantic retrieval tries to understand meaning. This is essential when agents ask complex questions that may not share vocabulary with the best source documents.

The CrewAI documentation for the Exa Search Tool describes semantic search as finding results based on meaning, not just keywords. It also shows why developers care about configuration. The tool supports search types such as auto, instant, fast, and deep. It can return highlights by default, include full page content when needed, filter by publication date, and restrict results to specific domains.

These controls allow a developer to adapt retrieval to the task. A financial research agent may need date filters. A support agent may need searches restricted to official documentation. A coding agent may need exact passages from repos and docs. A due diligence agent may need broad coverage and source verification.

The API matters because search becomes programmable. Developers are not just asking a search engine for results. They are composing retrieval behavior into an agent’s decision process.

Exa for coding agents

Coding agents are a strong example of why ordinary web search can struggle. Programming questions often depend on exact library versions, error messages, obscure documentation, GitHub issues, examples in repositories, and API behavior that changes over time. A blog post may not be the best answer. The best answer might be a small section of a docs page or a code example buried in a repository.

Exa’s product page says it powers coding agents with token efficient search across docs and repos. Its Series C announcement also says the company fine tuned special embedding models for code search. According to Exa, it was worse than Google at code search six months before that announcement, but later became used by nearly every coding agent.

Coding agents need search that can retrieve the right technical context quickly and cheaply. A model may be capable of writing code, but without the right documentation and examples, it can produce outdated or incorrect implementations.

Security and enterprise adoption

Agentic search becomes more sensitive when it enters enterprise workflows. Queries may include customer names, internal plans, private research questions, or regulated data. Exa’s product page emphasizes enterprise grade security controls, including customized zero data retention, automatic purging of queries and data based on requirements, and SOC 2 Type II certification.

That positioning matters because search for AI agents becomes part of the core business processes. If an agent researches prospects, supports customers, writes code, monitors competitors, or prepares internal briefs, the retrieval layer handles important context. Enterprises need controls over data handling, authentication, authorization, and compliance.

Exa says it serves hundreds of thousands of developers and thousands of enterprises. Its customer references include Cursor, Cognition, HubSpot, Monday.com, OpenRouter, and StackAI.

The funding signal behind Exa

Exa raised a major new funding at a $2.2 billion valuation led by a16z. Agents will soon search the web more than humans, and in the coming years agent search volume could become vastly larger than today’s human search volume.

The numbers are ambitious, but the strategic logic is understandable. If agents become common inside software, browsers, developer tools, CRMs, research platforms, support systems, and office workflows, they will need to retrieve information constantly. Search becomes a utility inside every AI system.

That could change the search market. Google search ads are built around human intent and commercial clicks. Agentic search may be monetized differently, through APIs, usage based pricing, …. Exa’s thesis is that the business of search for agents could become larger than traditional search advertising over time.

Where Exa fits in the AI stack

Exa is part of the AI infrastructure stack. Models generate and reason. Orchestration frameworks coordinate tools. Memory systems store context. Search APIs retrieve external knowledge. For many serious applications, the search layer determines whether an agent is grounded in reality.

Exa is particularly relevant for builders working on:

  • RAG systems that need current web evidence and citations.
  • Coding agents that search documentation, repositories, and technical pages.
  • Research agents that collect sources, create briefs, and verify claims.
  • Sales and market intelligence agents that need structured company and people data.
  • Enterprise assistants that require secure and configurable retrieval.
  • Automated workflows that need fast search inside multi step tasks.

What to watch as Exa grows

Exa’s opportunity is large, but the challenges are equally large. Building a true search engine means constant crawling, indexing, relevance research, infrastructure scaling, abuse prevention, and quality evaluation. The web changes every second. Agent workloads can spike unpredictably. Different verticals require different retrieval strategies.

Agents don’t need more information. They need better retrieval discipline. As models become more capable, the limiting factor is often whether they can find the right source at the right moment. Exa’s importance will be measured by how often it turns that moment into a reliable answer.