Why We're Building AnySearch

AI Agents Can Reason. But They Still Can’t See

Over the past two years, AI agents have improved their reasoning capabilities at an extraordinary pace.

They can now write code, conduct research, analyze companies, generate reports, and execute complex multi-step workflows. A growing share of work that once required direct human involvement can now be handled by agents.

But when we started building the search layer ourselves, we realized the core bottleneck wasn’t reasoning. It was information quality. A model may be excellent at analysis, but if the underlying inputs are noisy, outdated, distorted, or lacking context, the downstream reasoning process becomes increasingly unreliable.

The more capable the model becomes, the more critical it is to search quality.

An Amplification Effect That's Easy to Overlook

One counterintuitive reality is that stronger reasoning makes search quality more important, not less.

Humans naturally filter information when using search engines. We recognize SEO-heavy pages, skip low-quality aggregation sites, identify outdated content, and form quick judgments about source credibility.

Agents cannot do this reliably. Once search results enter an agent’s context window, they often propagate through every subsequent reasoning and execution step. Small retrieval errors can compound across long reasoning and execution chains. This is no longer an edge case.

As agents begin handling increasingly complex workflows, search is becoming a foundational dependency rather than a supporting feature. Single-source search struggles with cross-domain tasks. Meanwhile, search results are increasingly polluted by SEO content and low-signal aggregation.  To obtain reliable answers, agents must repeatedly invoke tools, validate sources, and filter stale information — driving both latency and cost higher.

At scale, search quality becomes a systems problem.

The Problem Started With How Search Was Designed

Most modern search systems were designed around human browsing behavior. They optimize for clicks, engagement, and advertising economics. Results are presented as links and summaries intended for human interpretation, not machine execution. That model works reasonably well for humans. For agents, it breaks down quickly.

There are several structural failure modes.

Misaligned incentives

Ad-driven ranking systems optimize attention, not answer quality. The highest-ranked result is often the page best optimized for distribution, not the one most useful for downstream agent workflows.

Information pollution

Search pipelines increasingly have to contend with SEO-generated pages, duplicated content, stale documentation, and low-signal aggregation sites.

Humans can quickly identify these patterns. Agents often cannot.

Human-Oriented Interfaces

Traditional search engines return pages designed for browsing. Agents need structured outputs, source attribution, freshness signals, confidence indicators, and data formats that can feed directly into execution workflows.

Search designed for browsing is fundamentally different from search designed for machine execution.

Is AI Search Enough?

It’s tempting to assume these are problems specific to traditional search, and that AI search already solves them. In practice, most AI search systems today are still built on top of conventional web search infrastructure.

Several limitations remain unresolved.

Real-Time Access

Most foundation models rely on static training snapshots. Access to real-time information — markets, flights, weather, infrastructure status, live events, or breaking incidents — remains fragmented and inconsistent.

Vertical Depth

Domains such as finance, law, healthcare, research, and industrial systems require highly specialized search pipelines and domain-specific data coverage. General web search rarely performs well in these environments.

Private and Semi-Open Data

A large portion of high-value information does not exist on the public web. It lives inside enterprise systems, structured databases, SaaS platforms, private communities, paywalled ecosystems, and application-layer data structures — environments traditional crawlers struggle to access effectively.

Execution-Unfriendly Outputs

Most search APIs still return content optimized for human consumption rather than machine execution. As a result, agents spend significant tokens on secondary cleaning, credibility assessment, and freshness filtering of search results —  work that should ideally happen at the search layer itself.

Traditional search suffers from misaligned incentives. Current AI search infrastructure still lacks the depth, structure, and reliability required for agent-native workflows.

Designing Search for Agents

Once the primary user of search shifts from humans to agents, many assumptions need to be reconsidered.

We believe the agent era requires a fundamentally different search infrastructure—not an API wrapper around a traditional search engine, but a system designed from the ground up for machine execution. Some principles are non-negotiable. Reliability matters more than engagement. Outputs should be directly consumable by workflows, rather than requiring additional interpretation by the model. Sources should be clear enough to trace and verify.

A search system built for agents should support:

• quality-first ranking
• resistance to SEO manipulation
• low-latency iterative workflows
• domain-aware search
• real-time and vertical data integration
• structured outputs
• transparent sourcing

The system should optimize for answer quality rather than engagement metrics. For agents, searching is no longer about discovering pages. It's about  acquiring reliable context for decision-making and execution.

Why We're Building AnySearch

This is why we decided to build AnySearch.

From the beginning, we didn't set out to build another search product optimized for clicks, traffic, or advertising. We wanted to build a search layer designed for developers, AI agents, and automated workflows.

As a result, our priorities are different. We focus on search quality, real-time access, structured outputs, workflow integration, and system reliability. AnySearch is designed as infrastructure first—not as a destination for human browsing. Building reliable search for agents is difficult. It requires continuously integrating and updating large-scale data sources, filtering noise, evaluating freshness and credibility, supporting real-time and domain-specific information, and delivering machine-readable results while maintaining low latency and predictable cost.

There are no shortcuts. These challenges have to be solved one layer at a time.

Search as Infrastructure

As agents become more capable, the role of search is changing. Search is no longer just an entry point to information. It is becoming part of the infrastructure agents rely on to understand and interact with the world. The next challenge isn't simply better reasoning. It's building systems that can reliably access, verify, and act on information from the real world.

That's the problem we're building AnySearch to solve.