# A deep-research mode, decoded

_Teardown_

Decoded from Gemini Deep Research (Google), ChatGPT deep research (OpenAI), Claude Research (Anthropic), Perplexity Deep Research (Perplexity).

## 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](/gradient_ascent/techniques/parallelization/) 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](/gradient_ascent/techniques/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](/gradient_ascent/techniques/orchestrator-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](/gradient_ascent/techniques/debate-review/) names, level 6. What
that checker actually checks matters, and the failure section comes back to it.

## Which page explains each part

| What you see | What it is | Page |
|---|---|---|
| A plan before the searches start, sometimes waiting for you to approve it | The loop's first move, decided by the model | [Agentic RAG and deep research](/gradient_ascent/techniques/agentic-rag/) |
| A source is read, then a narrower search follows | The loop deciding it does not have enough yet | [Agentic RAG and deep research](/gradient_ascent/techniques/agentic-rag/) |
| A panel, or an API response, listing the searches that ran | The run's own record of its tool calls | [Agentic RAG and deep research](/gradient_ascent/techniques/agentic-rag/) |
| Several searches going out at once | A fixed batch of calls, combined afterward | [Parallel calls](/gradient_ascent/techniques/parallelization/) |
| One model creating others to cover parts of the question | A lead agent handing out work | [Lead agent and workers](/gradient_ascent/techniques/orchestrator-workers/) |
| Every claim carries a citation | A separate pass, after the draft, locating what it already wrote | [Review and debate](/gradient_ascent/techniques/debate-review/) |

## 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](/gradient_ascent/techniques/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](/gradient_ascent/techniques/debate-review/) 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](/gradient_ascent/techniques/agentic-rag/), and it is enough to get a plan-search-read-decide loop working. Add [parallel calls](/gradient_ascent/techniques/parallelization/) 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](/gradient_ascent/techniques/debate-review/) 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](/gradient_ascent/recipes/research-brief/) takes the cheaper
route for the same job: agentic RAG for the searching, and [write and check](/gradient_ascent/techniques/evaluator-optimizer/), 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.


## Sources

1. [How we built our multi-agent research system](https://www.anthropic.com/engineering/multi-agent-research-system) — Anthropic, 2025-06-13 (accessed 2026-09-18)
2. [Deep Research](https://gemini.google/overview/deep-research/) — Google (accessed 2026-09-18)
3. [Introducing deep research](https://openai.com/index/introducing-deep-research/) — OpenAI, 2025-02-02 (accessed 2026-09-18)
4. [Introducing Perplexity Deep Research](https://www.perplexity.ai/hub/blog/introducing-perplexity-deep-research) — Perplexity, 2025-02-14 (accessed 2026-09-18)
5. [Claude takes research to new places](https://claude.com/blog/research) — Anthropic, 2025-04-15 (accessed 2026-09-18)
6. [Try Deep Research and our new experimental model in Gemini, your AI assistant](https://blog.google/products/gemini/google-gemini-deep-research/) — Google, 2024-12-11 (accessed 2026-09-18)
7. [Deep research](https://developers.openai.com/api/docs/guides/deep-research) — OpenAI (API documentation) (accessed 2026-09-18)


## Techniques it decodes into

- [Agentic RAG and deep research](/gradient_ascent/techniques/agentic-rag/) (measured): An agent that runs its own searches until it has an answer.
- [Parallel calls](/gradient_ascent/techniques/parallelization/) (sourced): Running several prompts at once and combining the results.
- [Review and debate](/gradient_ascent/techniques/debate-review/) (sourced): Agents that check, or argue with, each other's work.
- [Lead agent and workers](/gradient_ascent/techniques/orchestrator-workers/) (sourced): A lead agent splits the task and hands parts to other agents.

Last reviewed 2026-09-18. This teardown expires 2027-03-17.