research-agent

name	research-agent
description	Deep research with structured reports and charts. ONLY use when the user explicitly requests research/analysis, or needs data visualization with charts, or quantitative/comparative analysis across multiple sources. Do NOT use for simple questions or quick lookups.

Research Agent

Autonomous research agent that plans, searches across the web, synthesizes findings, and returns a structured markdown report with citations and charts.

When to use — ALL of these require explicit user intent or clear analytical need

• The user explicitly asks for "research", "report", "analysis", "deep dive", or "investigate"
• The user needs data visualization — charts, graphs, trend plots
• Quantitative or comparative analysis across multiple data points (market sizing, benchmarking, statistical comparisons)
• Multi-section structured reports (literature reviews, competitive analyses, technology surveys)

When NOT to use — default to simpler tools first

• General conversation, Q&A, or factual questions — answer directly
• A single lookup that wikipedia_search or google_web_search can resolve
• Summarizing a single article or URL — use fetch_url_content instead
• Code-related tasks — use the code-agent skill
• Browser automation — use the browser-automation skill
• Email, calendar, or other tool-based tasks — use the appropriate skill directly

Important: When in doubt, do NOT delegate to research-agent. Use google_web_search or other tools directly. Only escalate to research-agent when the task clearly requires multi-source synthesis, structured reporting, or chart generation.

How to invoke

Call the research_agent tool with a single plan argument. The plan is free-form prose; include:

• Objectives — what the user is trying to learn or decide
• Topics — the specific angles / subtopics to cover
• Structure — the section layout you want in the final report

Example:

research_agent(plan="""
Research Plan: AI Code Assistant Market 2026

Objectives:
- Current market size and growth trends
- Leading products and differentiators
- Enterprise adoption barriers

Topics:
1. Global market statistics and forecasts
2. Top products (Copilot, Cursor, Claude Code, etc.) and positioning
3. Pricing models and enterprise SKUs
4. Security/compliance concerns raised by buyers

Structure:
- Executive Summary (3-5 bullets)
- Market Overview
- Product Landscape
- Enterprise Adoption
- Outlook
""")

The agent streams research_step progress events as it works. The final result is a markdown report saved as a research artifact in the canvas.

Output

• Markdown report with #/## headings, bullet lists, and inline citations
• Any charts the agent generated are embedded in the markdown
• The full report is also persisted as an artifacts entry so the user can open it from the canvas

Guidelines for the orchestrator

• Don't fabricate the plan — use the user's own words and just structure them into objectives/topics/structure. If the user only gave a one-line request, expand it into 2-3 objectives but stay true to intent.
• One research_agent call per user request. Don't fan out multiple parallel calls.
• If the user asks a follow-up ("add a section on X", "dig deeper into Y"), call research_agent again with an updated plan — the agent itself does not have persistent memory across calls.
• After the tool returns, do NOT restate the whole report in chat. The report is already rendered as an artifact; a 1-2 sentence summary pointing the user to the canvas is enough.