Why multiple agents outperform one
A single general-purpose agent is a generalist. It can research, write code, and analyze data, but it switches contexts constantly and cannot work on two things at once. Specialized agents each do one thing extremely well: a Researcher agent has web search tools and is optimized for gathering information; a Developer agent has code execution and GitHub access and focuses entirely on engineering tasks; an Analyst agent processes structured data and generates reports. Specialization improves quality. Parallelism reduces time. Running a Researcher and a Developer simultaneously on different parts of a project delivers results that a single agent working sequentially cannot match.
Standard agent roles and what they do
Nova (Researcher): web search, article summarization, competitive research, news monitoring. Rex (Developer): code writing, debugging, test execution, git operations, GitHub integration. Atlas (Analyst): data analysis, spreadsheet processing, metric tracking, report generation. Orion (Integrations): connects external services, manages webhooks, runs automations via MCP servers. Aria (Writer): long-form writing, editing, email drafting, documentation. Leo (Data Analyst): SQL queries, data visualization, pattern detection in structured datasets. Each agent has role-specific tools and a system prompt that focuses it on its domain. You can assign tasks to specific agents or let a coordinator route work automatically.
How agent orchestration works
Orchestration is the process of routing tasks between agents and synthesizing their outputs. Three patterns cover most use cases. Sequential pipeline: Agent A completes its task and passes the result to Agent B — for example, Researcher gathers sources, Writer turns them into a draft, Developer publishes via API. Parallel execution: Multiple agents work on independent subtasks simultaneously — three Researchers covering different topics at once, or Researcher and Developer working in parallel on a product launch. Supervisor model: a coordinator agent breaks a complex goal into subtasks, assigns them to specialists, waits for results, and synthesizes the final output. All three patterns are supported in OpenClaw without any code — you describe the workflow in the dashboard.
Real workflows that benefit from multiple agents
Content production: Researcher gathers sources and trends, Writer drafts the article, Developer publishes via CMS API. Product development: Developer writes code, Analyst runs test coverage reports, Researcher investigates competitor features in parallel. Business intelligence: three Researcher agents monitoring competitor websites, pricing pages, and job postings simultaneously, feeding data to an Analyst that generates a weekly briefing. Customer operations: Orion monitors incoming support emails via webhook, routes technical questions to Developer and billing questions to Writer, who drafts responses for human review. Each of these runs continuously in the background — you check in on results, not on process.
Setting up a multi-agent team
On OpenClaw, agent selection is part of onboarding. After creating an account and entering your API key, you choose which agents to activate on your plan (Solo supports up to 3, Team up to 5, Studio up to 8). Each agent comes pre-configured with appropriate tools and system prompts for its role. You can customize agent instructions, assign them to projects, and define how they hand off work to each other — all through the dashboard interface. No orchestration code, no YAML config files, no infrastructure setup. The first multi-agent workflow can be running within 10 minutes of sign-up.
Ready to put this into practice?
Claw gives you a full AI team that handles this kind of work automatically.