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AI agents (examples: Claude Code, Deep Research, browser agents) do not search…

2026-07-21 · 15:21 UTC ·@adxtyahq ·1 min read

Brief

adxtyahq argues that decades of search infrastructure built for human query/scan/refine loops are ill-suited for AI agents, which fan out dozens of concurrent searches and synthesize results. While a 62ms search at $1/1,000 calls impressed them, they're focused on Octen's architecture—rebuilding the stack for concurrent agent workloads rather than offering a traditional search API.

Why it matters

AI agents (examples: Claude Code, Deep Research, browser agents) do not search linearly but 'fan out' into dozens of concurrent searches and then synthesize a single answer.

Key details

  • The author flagged a 62ms search at $1 per 1,000 calls as notable but argues those latency/cost numbers are secondary to architectural design.
  • Octen rebuilt the search stack around concurrent agent workloads instead of exposing a human-oriented search engine via API; the author is watching this architecture shift closely.
Cleaned source text

We've spent decades optimizing search for humans, but AI agents play by different rules

62ms search at $1 per 1,000 calls caught my attention, but I think the architecture is the bigger story

Claude Code, Deep Research and browser agents don't search linearly. They fan out into dozens of searches before synthesizing everything into a single answer

That's why Octen stood out to me. They rebuilt the search stack around concurrent agent workloads instead of exposing another human search engine through an API

The architecture shift is what I'm watching.

Kuan Zou (@KZouAPT)

Article

We stayed quiet and built the FASTEST search on Earth

Search was built for humans. You type one query, scan the results, refine, and try again.

Thirty years of infrastructure is designed around that loop.

AI agents DON'T work that way. An agent wants to

— https://nitter.net/KZouAPT/status/2079569508549673409#m