
Mistral AI
· 1 min read
Agentic Search. More accurate and efficient results from your AI systems.
Thinking
Summary
Mistral Agentic Search delivers more accurate search results while reducing turns, token use, and latency against FinanceBench and OfficeQA Pro benchmarks. Agentic Search is the retrieval layer that enables AI systems to navigate, read, and verify information inside even the most complex documents. Available through Mistral Search Toolkit and Libraries.
Mistral Agentic Search helps enterprises get better results from their AI systems by letting models search and navigate their organization’s most complex data and documents. Agentic Search introduces a multi-step retrieval loop for finding, inspecting, and verifying information across data sources, wherever it is stored. Agentic Search is available through Mistral Search Toolkit, built into Libraries in both Studio and Vibe, and gives you:
Support for sensitive domain-specific data. Mistral’s portable and open tooling helps you unlock value from your data without crossing your isolation boundaries in the cloud or on-premises.
Improved search results. Your models can search and navigate your data beyond retrieved chunks–inside long, dense documents or across multiple sources.
Access to existing indexes. Agentic Search builds on your existing search index using five tools:
search,open,navigate,read, andgrep.Higher accuracy. Agentic Search delivers to 3x correctness on financial filings, from 26.7% to 86%, based on FinanceBench. On table-heavy, multi-doc questions of the OfficeQA Pro benchmark, we measure a +45.6 point gain (6.3% to 51.9%).
Lower latency and token use. Targeted navigation enables Agentic Search to reduce p90 latency up to 39.6%. Fewer repeated searches reduce token consumption by up to one-third.
Data creates competitive advantage
Traditional RAG falls short
With Agentic Search
Trajectory 3 tool_calls (2× search → read)
search("national defense expenditures monthly 1953") → per-month bulletins (partial year)
read(treasury_bulletin_1954_02.pdf, p.15) → pulls the complete Table 3
Sum = 44,463.
Original source
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