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Published Jun 4, 2026
7 min read
Source Quality for AI Research Agents: More Search Is Not Better Research
Build reliable research with source tiers, freshness, conflict detection, citation coverage, and uncertainty.
Key takeawayA research agent should optimize evidence quality and traceability, not page count.
Why this deserves its own decision
Search results may repeat one original report or present stale material as current fact. An agent that summarizes the most common claim can manufacture false consensus.
Decision framework
- Prefer primary sources for technical, pricing, policy, and product facts.
- Record event date, publication date, and access date.
- Expose evidence conflicts and unresolved uncertainty for important claims.
Putting it into a ModelRush workflow
A planning route decomposes questions and source tiers, retrieval preserves URLs and excerpts, and the ModelRush synthesis route can use only collected evidence. A review route checks direct support for every key claim and labels inference.
What to measure after launch
- Primary-source coverage for key claims.
- Share of citations that do not directly support the claim.
- Stale facts, source conflicts, and labeled inferences.
A research agent should optimize evidence quality and traceability, not page count.
Next steps
Move straight from this article to model details, current pricing, API documentation, and the Playground.Hand the integration to an agent
Copy the full context so an engineering agent can inspect the stack and verify a request.Keep reading
Continue building the surrounding decisions in your multi-model stack.
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