A deep-research mode, decoded

What you see

Ask a question that needs more than one search to answer, and four products now do roughly the same thing: they go away, and several minutes later they come back with a long report, footnoted with citations, rather than a single paragraph. Gemini Deep Research, ChatGPT deep research, Claude Research and Perplexity Deep Research are the versions decoded here.

While it works, most of them show you something: a plan, a list of searches under way. When it finishes, the report reads like a person wrote it after doing the reading, with full sentences, a structure, and links back to specific pages. In between was a loop of small, ordinary steps: search, read, decide what is still missing, search again. The model ran that loop itself rather than your code, and the rest of this page takes it apart in this site’s terms.

What is happening underneath

Start with the smallest piece, the one your own code would recognize: several calls fired at once instead of one after another, combined afterward. Anthropic’s account of building Claude Research gives this as part of why the product is fast: “For speed, we introduced two kinds of parallelization: (1) the lead agent spins up 3-5 subagents in parallel rather than serially; (2) the subagents use 3+ tools in parallel.”[1] The second half is parallel calls in this site’s sense, level 3: a fixed batch dispatched together, so wall-clock time tracks the slowest call rather than the sum. The first half is a different thing under the same word, a model deciding for itself how many subagents to create, and that is level 6. It comes back below.

Strip the speed work away and what is left is the loop every one of these products runs: search, read, decide what is missing, search again or write the report. This is agentic RAG, one agent with a search tool, deciding for itself when it has enough. Google describes one turn of it: “At each step, the model has to ground itself on all information gathered so far, then identify missing information and discrepancies it wants to explore — all while trading off comprehensiveness with compute and user wait time”[2], continuing until “the model determines enough information has been gathered”[2]. OpenAI describes the same loop from the user’s side: “you give it a prompt, and ChatGPT will find, analyze, and synthesize hundreds of online sources to create a comprehensive report at the level of a research analyst”[3]. Perplexity says its version “performs dozens of searches, reads hundreds of sources, and reasons through the material to autonomously deliver a comprehensive report”[4]. Each page describes one model choosing its own next query. That is level 5: the model decides every step, and no second model decides for it.

Claude Research is the one to read carefully, because Anthropic described it two different ways. The launch post uses the same singular language as the others: “Claude operates agentically, conducting multiple searches that build on each other while determining exactly what to investigate next.”[5] Two months later, an engineering post describes a different shape for the same feature: “Our Research system uses a multi-agent architecture with an orchestrator-worker pattern, where a lead agent coordinates the process while delegating to specialized subagents that operate in parallel.”[1] A lead agent that creates other agents for parts of the question is lead agent and workers, level 6, a step above the loop Google, OpenAI and Perplexity document. Neither page says whether the feature changed shape between April and June 2025 or was built this way from the start and only described that way later. Read only the launch post and you would place it at level 5 like the others.

One more agent runs after the loop stops, at least in Claude Research: “the system exits the research loop and passes all findings to a CitationAgent, which processes the documents and research report to identify specific locations for citations. This ensures all claims are properly attributed to their sources.”[1] A separate agent checking material another agent drafted is the shape review and debate names, level 6. What that checker actually checks matters, and the failure section comes back to it.

Which page explains each part

What the makers say

Each maker states something about the loop that is easy to miss on a first read. Google’s launch post for Deep Research describes the plan step as something you act on rather than watch: it produces “a multi-step research plan for you to either revise or approve”[6] before any searching starts. OpenAI’s deep research API documentation says a finished run keeps a record of what it did, and the response “will contain a listing of web search calls, code interpreter calls, and remote MCP calls made to get to the answer”[7], logged even where a product’s chat interface does not surface every one. Perplexity puts a number on its own wall-clock time, completing “most research tasks in under 3 minutes”[4]. Anthropic prices the extra searching in tokens: “In our data, agents typically use about 4× more tokens than chat interactions, and multi-agent systems use about 15× more tokens than chats.”[1] Both numbers are a maker measuring its own product. None of these pages compares products.

Where it fails

Anthropic is the most specific about what went wrong before its system worked as intended. Watching early versions of its research agents step by step “immediately revealed failure modes: agents continuing when they already had sufficient results, using overly verbose search queries, or selecting incorrect tools.”[1] The first of those is the level-5 stop decision going wrong, and it is what agentic RAG’s own failure modes predict for anything built this way.

None of the four makers states how often a report’s citations turn out to be correct rather than merely present, and this page does not invent a number. What is checkable is scope. Anthropic’s sentence about the CitationAgent describes finding “specific locations for citations,” not confirming that a source supports the sentence a citation gets attached to. A citation that is present, formatted and pointed at a real page is not the same as a citation that is correct, and only the first is what Anthropic’s account says this pass does. Review and debate names the sharper version of the same gap: a reviewer built the way the author is built can share the author’s blind spot.

If you build one

Start at level 5. One agent, a search tool, and a hard cap your code enforces so the run ends whatever the model would have chosen next: that is agentic RAG, and it is enough to get a plan-search-read-decide loop working. Add parallel calls once a step needs several independent lookups at once. It buys wall-clock time, not a better answer, so add it after the loop works rather than before. Leave review and debate until a fixed, code-written check is not enough. Whether a citation names a real section that contains the sentence is a fixed check; whether the claim itself is true is not.

This site’s research brief recipe takes the cheaper route for the same job: agentic RAG for the searching, and write and check, level 3, for a fixed check of each drafted sentence against the section it cites. The recipe as a whole is level 5, and it is level 5 for the searching rather than for the checking. Move up to a reviewing agent only once the fixed check is shown to miss something it should have caught.