Research · Nov 1, 2026
Drex searches. Agents reason.
Nace.AI
TL;DR
An agent searching a filesystem can spend a surprising amount of its work deciding where to look. List the files. Search for a phrase. Read a match. Discover the wrong document or version. Search again. Each step asks the reasoning model to make another navigation decision, and each tool result eats up its context.
For instance, the answer to a query may hide in three rows of a table, on one page of a PDF, several folders deep. Locating those rows can take far more agent turns than reading them.
To solve this, we’re releasing Drex for Search. Drex is Nace’s System 1 decision LLM, built for lightning-fast, accurate decisions at high frequency. It is among the top performers on the Artificial Analysis Decision Index.
Drex for Search unifies retrieval and judgment into a single step. We delegate workspace navigation to Drex: organize the workspace as a tree, then use recursive Drex calls to judge which branches matter and locate evidence within them. This replaces the agent’s loop of ls, grep, and exploratory reads with precise pointers, so the agent can make surgical reads of the source.
In our evaluations on OfficeQA Pro and CUAD, Drex for Search reduced main-agent model calls by about 53% and 58%, respectively, compared with raw harnesses, and main-agent input tokens by about 57% and 43%. Mean answer scores were higher on both datasets. We also evaluated the same harness/model configurations with a third-party hybrid-search extension: Drex for Search used fewer main-agent iterations in every completed comparison. These workload figures cover the main agent’s work, excluding Drex inference.
How Drex for Search navigates a workspace
Drex for Search works over a Nace Document Intelligence (NDI) workspace. Files are ingested before search, making their internal structure available for navigation. A PDF exposes sections and source pages; an Excel workbook exposes named sheets, readable content, and queryable tables. Every location stays connected to its source.
Income statement
Revenue, expenses, and earnings for the reporting period.
Revenue by month
Reported revenue organized by monthly period.
The structure of the ingested NDI workspace is crucial to Drex for Search: it gives Drex a map to navigate recursively, narrowing from files to the evidence the agent needs.
A cascade of small decisions
Now consider a workspace containing annual reports, contracts, and a ledger. Take the query “What was total revenue for fiscal 2023?” as an example. Drex for Search then works as shown below.
See the entire search space.
Folders contain files; documents fan out into sections and pages; workbooks fan out into tabs and columns.
Scroll across to explore the full workspace
Before query-time decisions
0 total so farCards built from ingestion metadata: names, dates, summaries, headings, and table profiles.
Grey squares are pages. Outlined squares are columns. The 58 extra archive files are collapsed; the rest of the hierarchy is visible.
At each level, Drex judges what is relevant and where to look next. Repeating this process through the workspace makes the search recursive, leading to precise page, line, or table references the agent can use. Each stage narrows where to look next, keeping detailed inspection focused on promising branches. Independent decisions can run concurrently, so the main agent does not need a separate reasoning turn for every candidate considered.
The agent receives focused evidence and precise source references. It can then make surgical reads, reason over what it finds, and produce an answer grounded in the source.
More efficient search on real documents
We measured main-agent workload and answer quality on OfficeQA Pro (financial reports) and CUAD (commercial contracts). The charts compare the same harness, model, and reasoning effort on the same questions, with three ways to access the documents:
- Raw harness: the agent used its ordinary file tools over the text published by the datasets—OfficeQA’s official parsed text and CUAD’s full-contract text files.
- Raw harness + hybrid search: the agent kept those file tools and gained pi-knowledge-search, a third-party search extension, over the same parsed corpus. We integrated the exact same extension across all six harnesses: pi loads it directly, while Claude Code, Codex, Hermes, OpenCode, and Gemini CLI access its unchanged search and file-read tools through an MCP adapter, using the same corpus index. We used version 1.3.8, with
text-embedding-3-smallembeddings at 512 dimensions, BM25 keyword search, and reciprocal rank fusion. Each agent chose when to use the extension. - Drex for Search: the corpus was already ingested into our Nace Document Intelligence (NDI) workspace. Drex supplied evidence locations for the agent to read or query.
For each dataset, we created and ingested the NDI workspace once, before evaluation, and reused it across all question-answering runs. The hybrid-search index was also built once and reused. Workspace ingestion and index construction are outside the reported per-query measurements. The raw arm likewise starts with the published text already available.
An iteration is one request to the main agent’s model. Input tokens count everything sent to that model across its requests, including cached and repeated context. Workload reductions compare the sums of the per-configuration means within each dataset.
OfficeQA Pro
OfficeQA Pro asks questions over 697 U.S. Treasury Bulletin documents spanning 1939–2025. For the Drex for Search arm, the scanned PDFs were ingested into the NDI workspace before evaluation. The hybrid-search runs use the same web-enabled setting as the original raw runs.
Against the raw harnesses, across six harnesses and ten harness/model/effort configurations, Drex for Search reduced recorded main-agent iterations by 53% and input tokens by 57%. Both measures fell in every configuration. Accuracy rose in eight configurations and fell in two; mean accuracy moved from 59.3% to 61.9%.
All ten hybrid-search configurations have finished. Drex for Search used fewer main-agent iterations and input tokens than hybrid search in all ten, with higher accuracy in seven and lower accuracy in three.
CUAD
CUAD, the Contract Understanding Atticus Dataset, is a public collection of commercial contracts annotated for contract review. Our evaluation asks contract-clause questions over 200 contracts, using six harness/model configurations. Answer scores exclude failed runs.
We retain the original raw and Drex for Search results, replacing only raw Gemini CLI with Gemini 3.1 Pro with the new control run, which scores 88.7. The hybrid-search runs use the same questions, on a different harness build from the original evaluation.
Against the raw harnesses, Drex for Search reduced main-agent iterations by about 58% and input tokens by about 43%, calculated from the report’s rounded per-configuration means. Both measures fell in all six configurations. Answer scores improved in five configurations and fell slightly in one; the mean rose from 87.9 to 90.3 out of 100.
Compared with the hybrid-search arm, Drex for Search used fewer iterations in all six configurations and had higher answer scores in five. The agents could still use ordinary file tools, and the extension was used selectively; this evaluates access to a hybrid retriever within an agent loop.
Less exploration in the agent loop.
Each configuration shares a grey raw baseline, connected to its hybrid-search and Drex for Search results. Upper left means a higher answer accuracy with fewer agent iterations.
Hover or tap to isolate a configuration and compare all three arms.
Scroll across to compare workload and answer quality
The original raw and Drex for Search results are retained. Hybrid search was evaluated on the same questions with web tools on, as in the raw runs, using a different harness build.
From grep and RAG to Drex recursive decisions
Grep is a workhorse of agentic file search because it finds matching text quickly and predictably, especially for known identifiers and phrases. Hybrid retrieval adds another useful route: embedding search can find passages with different wording, while keyword search preserves exact matches.
With either grep or a RAG retriever, search in an agent system still follows a two-step process: the main agent writes a bash command or a retrieval request, then reasons over the results. Choosing what to search for and writing that command or request takes a model iteration.
When the results return, the agent reads them, judges what matters, and decides what to open or search next in a subsequent model iteration. If a match belongs to the wrong document, version, or period, the cycle repeats. Retrieval and judgment remain separate steps, coordinated by the main agent. A better retriever can improve the candidates; the agent still carries the query-and-inspection loop in its turns and context.
Agent-led exploration
With file tools or a RAG retriever
- 01Write a grep command or retrieval query
- 02Read and judge the returned candidates
- 03Choose what to open or search next
- 04Read, calculate, and answer
With Drex for Search
Navigation runs in the decision layer
- 01Read located evidence
- 02Calculate, reconcile, and answer
Some questions also leave the agent to discover how the source expresses the answer. A contract might describe renewal terms using different wording from the question. In OfficeQA Pro, a matching table can still belong to the wrong issue or reporting period. Finding a string and choosing the right evidence are different decisions.
Drex for Search unifies retrieval and judgment into a single step. At each level, Drex uses the question and the workspace’s structure to find and judge candidates together. Recursive Drex calls repeat that combined step through the workspace, replacing the main agent’s cycle of writing search commands and inspecting intermediate results. The agent can then focus on surgical reads, interpreting the evidence, reconciling figures, and answering the question.
We believe that combining retrieval and judgment within search—and moving navigation out of the main agent’s loop—helps explain the efficiency improvements we observe with Drex for Search on OfficeQA Pro and CUAD.
Drex searches - Agents reason
In a typical agentic search loop, grep or a retriever returns candidates, then the agent reads them and judges what to pursue. Drex for Search merges retrieval and judgment into a single step. Fast, inexpensive Drex calls repeat that combined step through the workspace, leading the agent to precise source references for surgical reads.
This is a promising direction for file search and filesystem agents: decision models find where to read; reasoning models turn that evidence into answers. Drex searches. Agents reason.