Agentic Search. More accurate and efficient results from your AI systems.

Introducing Agentic Search | Mistral Product Agentic Search. More accurate and efficient results from your AI systems. August 20, 2026 By Mistral

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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 , and grep .

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 Competitive edge is built upon years of real-world operations–your data, your processes, and your domain expertise. Proprietary knowledge is both critical to your success and highly confidential, meaning it lives behind isolation boundaries, segmented deployments, and self-hosted platforms. It accumulates in financial filings, legal contracts, internal resources, and government records–long, dense documents that traditional search methods can’t navigate effectively. Agents that learn and improve continuously can help you compound your competitive advantage, but these agents are often separated from confidential data and proprietary knowledge for security reasons. Getting real impact from AI means pairing frontier reasoning with retrieval tools that can safely reach your most sensitive material. Traditional RAG falls short Traditional, one-shot RAG retrieves a fixed set of text chunks and asks a model to answer in a single pass. This works when the answer appears in one of the top results, but falters when the model must navigate a long report, follow references, compare multiple documents, or verify the underlying evidence. The limitation is more pronounced on dense, complex data and documents. The information needed to answer a question may be spread across documents or buried in a particular table, footnote, or clause. One-shot RAG-based search fails to use the full power of frontier AI and to provide reliable answers for three reasons: Retrieval without reasoning: The model must answer from the chunks selected during the initial retrieval, even when they are incomplete or not relevant. It cannot decide that it needs a different document, another section, or more context before responding, which limits the impact of the model’s reasoning.

Chunk-level limit: Critical data is often held in complex multi-modal documents. When asked, “ What was the company’s effective tax rate in Q3?” an index may find the correct document but cannot open it, navigate to the table, read the surrounding context, or verify the answer.

No iteration: Many questions need more than one retrieval pass to get the correct answer. The model may need to refine its search, inspect a promising document, follow a reference, compare multiple sources, keep track of what it has seen, and try a new route when the first results are insufficient. One-shot RAG provides no way to take these next steps.

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--> Without Agentic Search (one-shot retrieval)

Using specifically only the reported values for all individual calendar months in 1953, what is the total sum of these values of expenditures for U.S. national defense and associated activities (in millions of nominal dollars)?

Trajectory 1 tool_call (search only)

search("national defense expenditures monthly 1953") → 10 hits: a scatter of monthly bulletins (Table 3), each framed fiscal-year, covering only part of 1953.

I found January–June 1953 data. But I need July–December 1953 monthly values to compute an answer.

With Agentic Search

Using specifically only the reported values for all individual calendar months in 1953, what is the total sum of these values of expenditures for U.S. national defense and associated activities (in millions of nominal dollars)?

Trajectory 3 tool_calls (2× search → read)

search("national defense expenditures monthly 1953") → per-month bulletins (partial year)

search("…1953 November December 1954 to date") → surfaces treasury_bulletin_1954_02.pdf p.15 (Table 3, all 12 months of 1953)

read(treasury_bulletin_1954_02.pdf, p.15) → pulls the complete Table 3

Monthly Values for 1953 Table 3, in $millions Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 3,632 3,501 3,789 3,891 3,746 4,056 3,890 3,519 3,787 3,647 3,540 3,465

Sum = 44,463.

How Agentic Search works Mistral Search Toolkit provides open modules for ingesting, embedding, and indexing critical and complex data in the cloud or on-premises. Agentic Search builds on this index by giving the model five tools that resemble familiar file-system operations: search finds relevant documents across the corpus using the existing index.

open opens a specific document.

navigate moves to a page, section, or region within it.

read retrieves the content at that location.

grep finds a pattern...