Research · Multi-agent demo

AI Research Agent

Plan → Source → Synthesize with collaborative AI teammates.

This demo orchestrates three specialized agents to turn loosely defined topics into structured plans, curated sources, and executive-ready reports across English and Chinese.

🔬Run the Research Workflow

Provide a topic, timeframe, focus areas, and optional web search. The planner, source hunter, and synthesis writer will coordinate automatically.

  • ✓Planner scopes the problem, defines guiding questions, and keeps deliverables on track.
  • ✓Source Hunter groups trustworthy citations from local knowledge or live search with metadata.
  • ✓Synthesis Writer composes concise reports with findings, risks, and recommended next steps.

✦Why this agent matters

AI Research Agent translates vague prompts into evidence-backed briefs. It blends planning, sourcing, and synthesis so teams can deliver market, academic, or technical insights faster.

  • Purpose-built for market analysis, academic reviews, and technical due diligence.
  • Reduces manual synthesis by combining planner, source hunter, and writer roles.
  • Supports UI demos, API integration, and SEO-optimized landing content.
Suggested long-tail keywords
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❖Multi-agent architecture

Three specialized roles coordinate through the research manager to avoid blind spots and dead ends.

  • Research Planner

    Drafts structured project briefs with guiding questions, deliverables, and risk checks so the run stays on target.

    collaborative ai agents for research planning
  • Source Hunter

    Surfaces and groups trustworthy citations from local knowledge or live search APIs, tagging each for later synthesis.

    ai source finder tool with web integration
  • Synthesis Writer

    Transforms plans and annotated sources into concise reports covering findings, risks, and recommendations.

    ai report synthesizer for structured outputs

End-to-end workflow

  • 11. Planning

    Planner scopes the task, aligns expectations, defines hypotheses, and flags data gaps before sourcing begins.

    step-by-step ai research workflow automation
  • 22. Source gathering

    Source Hunter optionally performs live web search, clusters citations with summaries, and attaches provenance metadata.

    ai agent pipeline for information synthesis
  • 33. Synthesis

    Writer merges the plan and sources into executive summaries, risk assessments, and practical recommendations.

    efficient multi-agent research process guide

Usage guide

Run from the interface

  • 1Open /research-agent or /zh/research-agent depending on your language.
  • 2Enter the topic and optional timeframe/focus areas to steer the planner.
  • 3Toggle web search when fresh sources are needed, then choose 1-12 results.
  • 4Select Run to orchestrate the agents and monitor progress inside the runner.
  • 5Review the plan, sources, and report; copy or export the sections you need.
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Output format

Each execution returns three sections designed for quick review and downstream automation.

  • Research plan

    Structured outline with objectives, key questions, and deliverables for alignment.

  • Reference sources

    Curated citations with summaries and metadata from local knowledge or live search.

  • Research report

    Markdown brief covering overview, findings, risks, recommendations, and references.

structured outputs from ai research agentsresearch report automation with citations

Representative use cases

Enterprise market assessment

Background: A strategy team sizes the consumer AI wearables opportunity and adoption risks.

Process: Planner narrows scope, Source Hunter analyses market reports and live sources, writer composes findings and risk radar.

Result: Executives receive a concise brief with quantified TAM, adoption triggers, and watch-list competitors.

Benefit: Supports faster go/no-go decisions without spinning up ad-hoc research sprints.

case studies of ai research agent in market analysis

Academic literature review

Background: A researcher explores ethical considerations in quantum computing for an upcoming paper.

Process: Run offline with local knowledge to surface relevant arguments, policy frameworks, and opposing viewpoints.

Result: Output organizes qualitative insights and references for immediate use in draft outlines.

Benefit: Compresses literature review time while keeping citations traceable.

real-world examples of multi-agent ai for academics

Competitive intelligence at scale

Background: An operations team integrates the API to monitor EV battery innovations each quarter.

Process: Recurring runs fetch up to 12 new sources, highlight market leaders, and flag supply-chain risks.

Result: Teams receive structured updates for procurement and product roadmaps.

Benefit: Automates continuous intelligence gathering for strategic decisions.

business intelligence ai agent success stories

FAQ

What is the AI Research Agent?+

It is a multi-agent system that automates research planning, source gathering, and report synthesis for markets, technology topics, and academic themes.

How do I enable web search?+

Check the web search option in the runner or set useWeb to true in the API payload to pull live sources via Serper/Tavily.

Which languages are supported?+

English and Chinese are available out of the box; additional locales can be added through next-intl.

How is accuracy maintained?+

Agents include source citations and cross-verify findings; users can manually audit the cited URLs.

What topics work best?+

Market analysis, technology trend scans, academic research, and business intelligence tasks are ideal.

Are there API limits?+

Rate limits depend on your deployment. Add authentication or quotas in production environments.

How do I customize research focus?+

Enter focus areas such as market, technology, risks, or regulation to guide the planner and writer.

Can I export the outputs?+

Yes. Copy the plan, sources, or report directly from the UI or store the API response as JSON.

What if the network is unstable?+

The agent retries when web search fails and falls back to local knowledge. Disable web search for offline runs.

Is the agent free to use?+

Local deployments are free. Cloud instances may require API keys for the underlying AI provider.

How do I extend the agent?+

Edit lib/research/agents.ts to add new roles or adjust prompts. Update the manager to incorporate new stages.

Does it support mobile devices?+

Yes. The Next.js layout is responsive and optimized for phones and tablets.

Extensibility & roadmap

Customize the agents

  • • Adjust planner, source hunter, or writer prompts in lib/research/agents.ts.
  • • Tune orchestration logic and output formatting inside lib/research/manager.ts.
  • • Extend lib/research/web.ts to plug in additional search providers or data sources.

Future directions

  • • Add multimodal inputs for charts, images, or transcripts.
  • • Enable collaborative runs with shared live sessions.
  • • Build knowledge graph enrichment for repeated topics.
  • • Automate quality scoring and completeness checks for reports.
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