Announcing Microsoft Web IQ | Bing Search Blog

Skip to content

AI applications are only as good as the information they reason from. Without fresh, high-quality web data, they are less dependable. Today, Microsoft is launching Web IQ: a suite of AI-native grounding APIs built for the agentic era, connecting AI systems and agents to fresh, real-world intelligence from across the web — including web pages, news, images, and videos.

The systems that define the agentic era will be the ones that can retrieve fresh, authoritative evidence quickly, transform it into useful context, and do so within the latency and efficiency budgets that multi-step reasoning demands.

Model capability alone no longer determines whether an AI system is useful. What matters is how effectively the full system connects models to the world, including information created after models were trained, and information too vast to encode in model weights.

Web IQ is a search engine for AI systems. Where Bing was built to help people search the web, Web IQ is built to help AI agents find the right information, turn it into useful evidence, and use it inside reasoning. Unlike other APIs that layer on top of fragile infrastructure, Web IQ is a new kind of search system, one that delivers the right evidence with the speed, quality, and efficiency modern agents require.

It builds on years of learning from Bing, but it required a major ground-up re-architecture to meet the demands of agentic workloads.

Built on Bing, Re-Architected for the Agentic Era

Web IQ starts from the foundation Microsoft has been building for decades: the Bing global index and ecosystem. Grounding quality depends on the breadth, freshness, and trustworthiness of the world representation underneath it – something that is achieved by building on Bing’s expansive reach.

But the agentic era asks fundamentally different questions of the stack. Agents do not issue a single search and stop. They retrieve repeatedly, reason over evidence, adapt to new information, and operate inside tight latency budgets. Meeting those requirements could not be solved by tuning a single component. It required re-architecting the system from the ground up from indexing and retrieval to ranking, passage selection, and orchestration so every layer is aligned around the needs of inference-time grounding. That is the core idea behind Web IQ: preserve the strengths of Bing’s foundation while redesigning the grounding stack to serve as the execution fabric for AI agents.

At the base of this system is something that predates Web IQ by many years: the Bing global index and ecosystem.

Evolving beyond a large crawl, it is a continuously refined representation of the web, built over decades through a combination of infrastructure, partnership, and discipline. It reflects millions of decisions about what to include, how to rank it, how to ensure freshness, and how to maintain trust.

That discipline extends to how we participate in the open web itself. Web IQ inherits Bing’s long-standing commitment to the conventions and evolving standards of the internet ecosystem, including honoring robots exclusion protocols, publisher controls, and access preferences that govern how content can be discovered, accessed, and used. We are actively engaging with the broader ecosystem through the IETF and other industry forums to help evolve interoperable standards for the AI era. Our goal is to be a sincere and trusted participant in the open web — one that respects publisher choice and helps sustain a healthy ecosystem for content providers, advertisers, developers, and users alike.

The role of that foundation is often underestimated. Grounding systems cannot exceed the quality of the world they observe. If the index is incomplete, stale, or unreliable, no amount of modeling can compensate. Web IQ begins from the premise that grounding quality is anchored in the quality of the underlying corpus and that corpus must be global, fresh, honor publisher preferences by default, and continuously evolving.

On top of that foundation sits the model layer, where we made a different kind of decision.

Rather than building a large collection of specialized models, we focused on a small number of models that are world-class and tightly integrated into the system . These models serve distinct but coordinated roles: they analyze content, they represent it in embedding space, and they rank and select it for use inside inference.

One of the central components here is our best-in-class embedding model, which defines how information is projected into a space where semantic similarity becomes computationally tractable. That decision alone has far-reaching consequences; the quality of embeddings determines not just recall during retrieval, but the shape of the candidate space that every downstream component operates on. We have built it to be competitive at the top of public benchmarks, but its role inside Web IQ is...