Build a Multi-Agent Research System in Under 10 Minutes
Deploy coordinated AI agent networks that gather, validate, synthesize, and report on complex topics—automatically. Architect orchestrates specialist agents so your team gets deep research without the manual grind.
Research Automation Statistics
Why It Matters
Manual research is slow, expensive, and inconsistent. A single analyst can only process so many sources before quality degrades. Multi-agent systems run in parallel, validate each other, and produce synthesis-ready outputs at a scale no human team can match—giving your organization a permanent research advantage.
Connects to Your Research Stack
Architect agents pull from and push to the tools your team already uses—no custom connectors required.
Platform Capabilities
Design networks of specialist agents—each with a defined role, memory scope, and tool set—and configure routing, parallel execution, and handoff logic through a visual builder. No orchestration code required.
Equip search agents with web search tools, API connectors, and document loaders. Agents retrieve up-to-date information from live sources, internal knowledge bases, and structured databases simultaneously.
Deploy dedicated validation agents that cross-reference claims across multiple sources, flag inconsistencies, and score credibility—so every output includes confidence signals your team can trust.
Synthesis agents consolidate validated findings into structured reports with citations, executive summaries, and actionable recommendations—delivered directly to Slack, Notion, email, or any webhook endpoint.
Agents retain context across research runs using configurable short-term and long-term memory stores. Build longitudinal research programs where each session builds on prior knowledge without starting from scratch.
Architect enforces guardrails at every agent layer—role-based access controls, audit logs, PII redaction, and configurable output filters—so research pipelines meet enterprise compliance standards from day one.
How It Works
Submit a research brief, question, or topic via UI, API, or scheduled trigger. Set scope, depth, and output format preferences.
Search agents fan out across web APIs, knowledge bases, documents, and databases in parallel—maximizing coverage and recency.
Validation agents verify claims across sources, score credibility, and flag conflicts—passing only high-confidence findings forward.
A synthesis agent compiles findings into a structured report with citations and summaries, then delivers it to your chosen destination automatically.
Before vs After Architect
- Analysts spend 8-12 hours manually compiling research on a single topic
- High cost per research report due to senior analyst time and tooling sprawl
- Research quality varies with analyst availability, expertise gaps, and cognitive load
- Blind spots persist as teams cannot monitor all relevant sources continuously
- No audit trail or consistent citation format across research outputs
- Full research report delivered in minutes via parallel agent execution
- 70% cost reduction by replacing manual hours with automated agent pipelines
- Consistent, validated outputs with credibility scoring on every source
- Continuous 24/7 monitoring across all configured sources and topics
- Full audit trail with citations, timestamps, and source URLs in every report
Sample Agent System Prompt
Architect Agent Configuration
You are the Orchestrator Agent in a multi-agent research system built on Architect. Your responsibilities: 1. Receive the research topic or question from the user or upstream trigger. 2. Decompose the topic into sub-queries and dispatch them to Search Agents. 3. Coordinate parallel execution — do not wait for one agent to finish before dispatching others. 4. Pass retrieved source batches to the Validation Agent for credibility scoring. 5. Accept only sources with a credibility score >= 0.75 for synthesis. 6. Instruct the Synthesis Agent to produce a structured report: executive summary, key findings (cited), confidence levels, and recommended next steps. 7. Deliver the final report to the configured destination (Slack, Notion, or webhook). 8. Log all agent actions, sources consulted, and output tokens to the audit trail. Tone: objective, factual, citation-backed. No unsupported claims.
Frequently Asked Questions
A multi-agent research system uses multiple coordinated AI agents—each with a specialized role such as data gathering, source validation, synthesis, and report generation—to automate complex research workflows end-to-end. Each agent focuses on what it does best while Architect handles the orchestration between them.
Architect provides a visual, no-code builder for designing and deploying networks of AI agents with defined roles, memory policies, tool access, and handoff logic. Teams configure production-grade research pipelines in under 10 minutes—without writing orchestration code.
Architect agents connect to web search APIs, internal knowledge bases, Notion, Google Drive, Confluence, Slack, relational databases, vector stores like Pinecone, REST APIs, and more—via built-in integrations or custom tool definitions you configure in the platform.
Yes. Architect lets you define specialist agents for each subtask—search, validation, summarization, citation management, and report drafting—and configure the orchestration logic that determines routing, handoffs, and parallel execution order.
No. Architect is a no-code platform. You configure agents, define tools, set memory policies, and deploy production pipelines entirely through a visual interface. Engineering resources are optional—not required—to get a research system live.
Ready to Deploy Your Research Agent Network?
Join teams already running production multi-agent research systems on Architect. Set up your first pipeline in under 10 minutes—no code, no infrastructure headaches.