Native web search and agent-native search optimize for different users.
Native web search is for humans.
Agent-native search is for models that need to act.
That difference changes the benchmark.
What Native Web Search Returns
Native search often returns:
- links
- titles
- snippets
- full pages
- SEO-ranked results
Humans are good at filtering this.
Agents are not humans.
They consume context and act on it.
What Agent-Native Search Returns
Agent-native search should return:
- compact evidence
- source URLs
- source type
- authority ranking
- low boilerplate
- enough context to act
The goal is not browsing.
The goal is decision support.
Benchmark Metrics
Measure:
- first-shot resolution
- source quality
- tokens per useful answer
- number of follow-up searches
- number of failed edits
- human correction rate
Latency matters, but it is not the only metric.
A fast bad result creates slow workflows.
Where Ninelayer Fits
Ninelayer is built for the agent-native side.
It focuses on compact, source-aware retrieval through MCP so coding agents and research agents can use web context before acting.
The Practical Takeaway
Do not benchmark agent search like consumer search.
The question is not whether the user clicked a link.
The question is whether the agent got enough trustworthy context to do the task right.
Sources
- Ninelayer blog: Why Search Is the Missing Layer for AI Agents
- Ninelayer: Performance benchmarks