Research & Synthesis Agent

What a research agent actually does

You define a research task — "produce a 2-page competitive intel brief on company X covering pricing, product roadmap, team, funding, and customer reviews from public sources." The agent runs the research: queries search engines, fetches pages, extracts relevant chunks, dedupes, summarises, structures into your output format, and emits a dossier with inline citations.

You can run it interactively (a salesperson before a call) or on a cadence (50 prospect briefs every Monday morning).

The defining features versus a generic LLM with browsing:

• Defined task with structured output schema, not free-form chat
• Source dedup so 12 articles about the same news event don't all influence the summary
• Citation map so every claim is traceable to a source
• Date awareness so stale claims don't override recent ones
• Repeatable — the same input produces a comparable output, run after run

Anatomy of a working pipeline

[Research task with schema]
      ↓
[Query expansion: 5–15 sub-queries from the task]
      ↓
[Parallel search across sources: Google, Bing, internal KB, structured APIs]
      ↓
[Headless browser fetch (Browserbase / Firecrawl) for top results]
      ↓
[Chunk + embed + dedup across sources]
      ↓
[Long-context synthesis (Claude) with all evidence in window]
      ↓
[Structured output (Zod schema) with inline citation IDs]
      ↓
[Citation map: every claim → list of source URLs + dates]
      ↓
[Output to destination (CRM, Notion, email, dashboard)]

For deeper RAG / retrieval patterns we use, see our RAG patterns post.

Use cases we have shipped

• Sales prospect briefs. Salesperson opens HubSpot contact → "Generate brief" button → 2-page brief on the contact's company appears within 90 seconds, attached to the record. Inline citations link to source articles.
• Competitive intel monitoring. Daily scheduled run for ~30 competitors; agent surfaces changes (new pricing tier, hire announcement, product launch) in a Slack digest.
• Due-diligence dossiers. Investment team requests a brief on a target company; agent produces 5–10 page dossier covering market, product, team, funding history, customer reviews, regulatory exposure, with cited sources.
• Customer-success research. Before a QBR, agent compiles a one-pager on the customer's recent news, hiring, expansion signals.

Stack we tend to reach for

What makes a research agent "production-grade"

• Schema-driven output. Not free-form text. The output format is defined upfront and validated.
• Source dedup. Cosine similarity between chunks; chunks above threshold collapse into one with multiple sources.
• Date weighting. Recent sources outweigh older sources for time-sensitive claims.
• Citation map. Every claim has at least one source attached. No source = no claim.
• Fail-safe. If the agent cannot find enough sources or hits low confidence, it flags the section instead of confidently fabricating.
• Auditable. The full chain — query, search results, fetched pages, chunks selected, prompt, output — is logged and replayable.

Cost and timeline

Pass-through costs: ~€0.20–€3 per dossier depending on depth, plus Browserbase / search API costs.

Where it pairs

Research agents commonly chain with:

• Workflow orchestrators that decide what to research and what to do with the output (route to a CRM, email a digest, file a ticket).
• Conversational agents that let users query the research corpus interactively after it's been built.
• Document processing agents when the research outputs themselves need extraction (e.g. extracting structured pricing tables from competitor sites).

If you have a research workflow your team currently does manually, drop us a note. One paragraph is enough.