How an LLM stops being a chatbot and becomes an analyst — the orchestrator + subagent pattern that now powers ChatGPT Deep Research, Anthropic Research, Perplexity, and Gemini.
01 — Core Concept
What "deep research" actually is — and isn't
Throughout 2024, "agent that browses the web" mostly meant a single LLM that could call a search() tool. That works for "what's the weather in Tokyo." It does not work for "compare the GTM strategies of the top 5 European AI compliance startups, with citations." For the second kind of question, by early 2025 a new architecture had crystallized — and every major lab shipped a product version of it within four months of each other. OpenAI's Deep Research launched February 2, 2025. Perplexity Deep Research followed February 14. Google's Gemini Deep Research had been in preview since December 2024. Anthropic's Research mode shipped in April 2025 and the team published a remarkably candid engineering write-up in June. The pattern they all converged on is what this lesson is about.
The analogy first. A chat agent is a junior associate you Slack-message — fast, conversational, single-shot. A deep research agent is a research director who delegates to a small team. The director reads your prompt, decomposes it into sub-questions, hires three or four interns to chase down each thread in parallel, gets their findings back, decides whether more digging is needed, and then writes the final report with citations. The director never reads the raw web pages — that's the interns' job. The director reads summaries. This split is the whole game.
Now precise. A deep research agent is a multi-stage system in which a lead agent (also called orchestrator, coordinator, or planner) decomposes a user query into research subtasks, dispatches them to subagents running in parallel, each of which executes its own ReAct-style loop over web tools (search, fetch, read), returns a structured finding to the lead, and the lead either iterates (more subagents) or synthesizes (writes the report). The August 2025 survey by Zhang et al. (arXiv 2508.12752) formalizes this as a four-stage pipeline: planning → question developing → web exploration → report generation. Anthropic's June 2025 engineering blog reports their multi-agent system with a Claude Opus 4 lead and Claude Sonnet 4 subagents outperformed single-agent Claude Opus 4 by 90.2% on their internal research eval — the most-cited number in the field.
1Three properties that distinguish deep research from RAG
Open-ended retrieval. RAG (Day 4) retrieves from a known, indexed corpus. Deep research retrieves from the open web — the search space is unbounded, sources are heterogeneous (HTML, PDF, JSON, paywalled), and freshness matters. The subagent must decide what to search for next based on what it just read.
Multi-turn iterative refinement. A deep research run averages 30-50 search calls and 100-200 page reads, executed across 5-30 minutes of wall-clock time. Per Anthropic's blog, their research agents use roughly 15× more tokens than a normal Claude chat. This is not a performance bug — it's the product. You're paying for breadth and depth.
Citation-grounded synthesis. The output is a structured report with inline citations to specific URLs. Hallucinated citations (a recurring failure mode) are a P0 bug, not a quality issue. This forces a discipline: the synthesizer has to be able to say "I don't have evidence for X" rather than fill in plausible-sounding text.
Mental model: RAG = "look up the answer in the company wiki." Deep research = "open Chrome, search, click, read 80 tabs, and write me a memo."
2Why parallel subagents beat a single long-context call
The naive alternative — stuff every search result into a 1M-token context window and let the model reason over it — sounds appealing and mostly does not work. Two reasons.
Attention dilution. Performance on retrieval-from-long-context degrades non-linearly with context length. The "needle in a haystack" plots look great; the "two needles in a haystack" plots fall off a cliff. Real research questions need 5-20 distinct findings, not 1.
Compute parallelism. Five subagents browsing in parallel finish in roughly 1/5 the wall-clock time of one subagent doing all the work serially. For products charging for 5-30 minutes of agent time, this is the difference between shipping and not shipping.
The Anthropic blog quantifies this — their internal eval credits "token-budget-allocation across multiple agents" with the bulk of the 90.2% improvement, not better prompting of any single agent.
The four-stage pipeline — in one diagram
┌──────────────────────────────────────────┐
│ STAGE 1 · PLAN │ // lead agent
│ user query → research plan + sub-tasks │
└──────────────┬───────────────────────────┘
│
▼
┌──────────────────────────────────────────┐
│ STAGE 2 · QUESTION DEVELOPING │ // lead agent
│ decompose → 3-8 specific sub-queries │
└──────────────┬───────────────────────────┘
│
▼ spawn N subagents in parallel
┌────────┐ ┌────────┐ ┌────────┐ ┌────────┐
│ SubA-1 │ │ SubA-2 │ │ SubA-3 │ │ SubA-N │ // each runs ReAct loop
│ search │ │ search │ │ search │ │ search │ // search(q)
│ fetch │ │ fetch │ │ fetch │ │ fetch │ // fetch(url)
│ read │ │ read │ │ read │ │ read │ // read(content)
│ summz │ │ summz │ │ summz │ │ summz │ // summarize → return
└───┬────┘ └───┬────┘ └───┬────┘ └───┬────┘
│ │ │ │
└───────────┴─────┬─────┴───────────┘
▼ STAGE 3 · WEB EXPLORATION (returns)
┌──────────────────────────────────────────┐
│ STAGE 4 · REPORT GENERATION │ // lead agent (or writer)
│ N findings → cited synthesis report │
│ (gap detected? loop back to Stage 2) │
└──────────────────────────────────────────┘
Two architectural details worth burning into memory. First, the lead agent never reads raw web pages — it reads structured findings (typically 200-500 tokens per subagent return). This keeps the lead's context window healthy across 20+ subagent invocations. Second, the loop in Stage 4 ("gap detected → re-plan") is what makes "deep" research deep. A single shot is not enough — the lead needs to look at the assembled findings and notice "I have nothing about pricing" or "two sources contradict each other on revenue," then spawn another wave of subagents.
~15×
Tokens per query vs. normal chat (Anthropic, June 2025)
90.2%
Multi-agent uplift over single-agent on Anthropic's internal eval
5-30 min
Wall-clock time per OpenAI Deep Research run
02 — Papers to Know
Four primary sources that define this field
SURVEY · 2025
arXiv preprint 2025
Deep Research: A Survey of Autonomous Research Agents
Wenlin Zhang, Xiaopeng Li, Yingyi Zhang, Pengyue Jia, Yichao Wang, Huifeng Guo, Yong Liu, Xiangyu Zhao · arXiv:2508.12752 (Aug 2025)
The first systematic taxonomy of the deep research pipeline. Defines the four canonical stages — planning, question developing, web exploration, report generation — and surveys 80+ implementations against this skeleton. For each stage, it catalogs the technical challenges (e.g., decomposition granularity, search-query reformulation, source ranking, citation accuracy) and the methods proposed to address them. If you read one paper, this is the one — it's the rosetta stone for the rest of the literature.
Why it matters: Before this survey, every product team described their deep research pipeline differently. The four-stage taxonomy now anchors how teams compare implementations, decompose failures, and prioritize improvements. "Stage 2 failure" is now a thing engineers can say in standup.
arxiv.org/abs/2508.12752
SEMINAL · 2025
arXiv preprint 2025
BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agents
Jason Wei et al. (OpenAI) · arXiv:2504.12516 (April 2025)
1,266 questions that require persistent multi-hop web navigation to answer — questions like "What was the first paper to cite this 2003 conference proceedings on graph algorithms?" Answers are short and verifiable; the difficulty is in the navigation. GPT-4o without browsing scores 0.6%; with browsing it climbs only to 1.9%. The early Deep Research model was the first system to break double digits. The benchmark now anchors the entire field — every paper in this list reports a BrowseComp number.
Why it matters: Made deep research evaluable. Before BrowseComp, "is my deep research agent good?" was a vibe check. After BrowseComp, it's a number you can put on a dashboard and a value you can train against with RL.
arxiv.org/abs/2504.12516
RECENT · 2025
arXiv preprint 2025
Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning
Bowen Jin et al. · arXiv:2503.09516 (March 2025)
Built on the veRL framework, Search-R1 trains a model to interleave reasoning steps with search-engine calls, with rewards shaped from final-answer correctness. On seven QA datasets, it improves performance by 41% on Qwen2.5-7B and 20% on Qwen2.5-3B over RAG baselines. The takeaway is that you do not have to prompt-engineer a search agent — you can train one, the same way DeepSeek trained R1, just with the search tool inside the loop.
Why it matters: Showed that "agentic search" is a fine-tunable skill, not a prompting trick. Opened the door to the wave of RL-trained search agents that followed (R1-Searcher, WebDancer, WebSailor) — the recipe that now powers most open-weight deep research models.
arxiv.org/abs/2503.09516
RECENT · 2025
arXiv preprint 2025
WebDancer: Towards Autonomous Information Seeking Agency
Tongyi Lab, Alibaba · arXiv:2505.22648 (May 2025)
A four-stage post-training recipe that takes an open-weight base model and turns it into an autonomous web-research agent: (1) browsing-data construction, (2) trajectory sampling, (3) supervised fine-tune cold start, (4) RL on the agent loop. Evaluated on GAIA and WebWalkerQA, the result surpasses GPT-4o in best-case scenarios — using a much smaller open-weight backbone. Spiritually a sibling to Search-R1, but at full agent-trajectory granularity rather than just search-call granularity.
Why it matters: Together with WebSailor (its successor), this is the playbook for building a state-of-the-art deep research agent without OpenAI's data or compute budget — and the work behind why Tongyi Deep Research is now genuinely competitive with the closed frontier on browsing benchmarks.
arxiv.org/abs/2505.22648
ENGINEERING BLOG · 2025
How we built our multi-agent research system
Anthropic Engineering · June 2025
Not a paper — but the most-cited engineering write-up in the field. Documents the lead-researcher-plus-subagents architecture behind Claude's Research mode, the prompt-engineering lessons (vague subagent objectives → duplicated work, e.g., one subagent investigating the 2021 chip crisis while two others duplicate effort on 2025 supply chains), and the headline 90.2% multi-agent uplift over single-agent on their internal eval. The clearest free public explanation of how a frontier-lab deep research agent actually works under the hood.
Why it matters: Engineers reading this blog will recognize 80% of the design choices in their own products. Anthropic published it specifically because they noticed teams kept reinventing the same wheel poorly.
anthropic.com/engineering/multi-agent-research-system
04 — Community Pulse
What practitioners are saying — last 60 days
Anthropic Engineering — June 2025 blog
"Each subagent needs an objective, an output format, guidance on the tools and sources to use, and clear task boundaries... vague instructions like 'research the semiconductor shortage' often were vague enough that subagents misinterpreted the task or performed the exact same searches as other agents — with one subagent exploring the 2021 automotive chip crisis while 2 others duplicated work investigating current 2025 supply chains."
The most-cited paragraph in the most-cited engineering blog of 2025. Source: anthropic.com/engineering/multi-agent-research-system
Harrison Chase (LangChain CEO) — 2026
Chase has argued that what makes "deep agents" deep is the sophistication of the looping architecture — planning, context management, memory, subagents, and richer prompting — and that the "harness" surrounding the model is now where most production value gets built, not the model itself. LangChain's bet on Deep Agents as a category (with Deep Agents UI, deep_research_from_scratch, and an Interrupt 2026 keynote) operationalizes this view.
Paraphrased from LangChain's 2026 blog series and Sequoia podcast appearance.
Simon Willison — Feb 2025 → Apr 2025
Willison's posts on Perplexity Deep Research (Feb 16, 2025) and "AI assisted search-based research actually works now" (April 2025) marked the inflection point in practitioner sentiment — the public moment when a generally-skeptical voice said the search-based research category had crossed from "almost works" to "useful for real work."
Source: simonwillison.net/2025/Feb/16/introducing-perplexity-deep-research/ and the April Substack follow-up.
Andrej Karpathy — 2025 LLM Year in Review
Karpathy framed 2025 as the year RLVR (Reinforcement Learning from Verifiable Rewards) became the de facto post-training stage, and noted Claude Code as "the first convincing demonstration of what an LLM Agent looks like — something that in a loopy way strings together tool use and reasoning for extended problem solving." Deep research agents are the search-loop sibling of this same recipe: the same RLVR machinery, applied to a verifiable-answer search environment instead of a code environment.
Source: karpathy.bearblog.dev/year-in-review-2025/
08 — CGO Lens
What today's topic means for botlearn.ai
1Deep research is the highest-margin agent product right now
OpenAI charges $200/month for Pro (250 deep research queries) — about $0.80 per query. Anthropic's Claude Pro tier exposes Research at a similar implicit per-query margin. These products use ~15× more tokens than chat but customers gladly pay 5-10× more for them, because the output looks like analyst-grade work. For botlearn.ai's curriculum-generation use case, this margin profile is the most attractive in the agent product landscape.
2The buy-vs-build decision changed in Q1 2025
Through 2024, a deep research agent was a science project — no team without a frontier-lab background could ship one. By Q1 2025, OpenAI's launch + the smolagents 24-hour replication showed it was a 2-3 engineer-month project, given the right base model. By mid-2025, Tongyi's WebSailor open-sourced the full training recipe. For an education company that wants its own research agent (own brand, own data, own pricing), build is now realistic. The strategic question is whether your differentiation lives in the agent or in the corpus the agent reads.
3Cost has the same shape as a SaaS storage curve
OpenAI's o3-deep-research API was the first move that turned deep research from a consumer feature into a developer building block — meaning every B2B SaaS will start adding "research" buttons in 2026. The competitive question is not "do we have a research feature?" — within twelve months, every product in your category will. The question is what makes a research output uniquely defensible in your vertical. For botlearn.ai, two candidates: (a) curated source list specific to AI/agent education that no general-purpose research agent ranks, and (b) a research-output format that compiles into a didactic asset (lesson, deck, video script) — not a generic report.
4The China factor — directly relevant for an Asia-facing CGO
Tongyi Lab's WebSailor and the Alibaba-NLP/DeepResearch repo mean that for a China- or APAC-targeted product, a fully open-weight, self-hosted deep research agent is now a real option. WebSailor-72B reports 12.0% on BrowseComp-en and 30.1% on BrowseComp-zh — the Chinese-language number is competitive with the closed frontier. For botlearn.ai's Xiaohongshu / Chinese-market presence, an open-weight self-hosted research agent removes both the API tax and the data residency concern in one move.