From SERPs to Agent Infrastructure
Product manager at Cohere on enterprise AI search infrastructure and deep research agents
The real split here is between tools that fetch web results for a model, and tools that try to run part of the research job itself. Parallel is being described as infrastructure for long, multi step agent workflows, where the system searches, reads, refines the query, and assembles a report over minutes. Tavily is closer to a fast retrieval layer that gives an agent fresh web context, extracted text, and ranked snippets in a single API call, which is easier to plug into an existing stack.
-
In practice, Cohere uses Tavily for web search inside North because it returns usable website text and grounding with less post processing. Earlier options like Brave required extra hops to fetch and clean page content before a model could use it.
-
Parallel shows up more often in deep research workflows. In Manus, it handles broad, segmented investigations that can take 10 to 15 minutes and produce tables, comparisons, and source backed writeups. That is meaningfully closer to an agent runtime than a plain SERP API.
-
Exa sits between the two. It sells search, crawl, answer, research, and Websets products, and its Websets API runs asynchronous searches that verify and enrich results. That supports the view that the category is expanding from search endpoints into higher level web infrastructure for agents.
The market is moving toward bundled agent infrastructure, not just better link retrieval. The winners are likely to be the vendors that combine search, extraction, crawling, research, and structured outputs in ways that save developers from building multi step web workflows themselves. That pushes narrow SERP style APIs to compete more on price and ease of integration.