An AI agent team is not one big prompt doing everything. It is a group of specialized agents, each with a clear role, collaborating on tasks the way a human team would.
This guide walks through how to build one from scratch.
Step 1: Define the roles
Start with the work you want automated. Break it down into distinct functions:
- Research - gathering information, monitoring sources, pulling data
- Analysis - interpreting data, identifying patterns, making recommendations
- Writing - producing content, documentation, reports
- Execution - taking actions in external tools (creating issues, sending messages, updating records)
- Review - checking output quality, enforcing standards
You do not need all of these. Most teams start with two or three agents.
Step 2: Write focused system prompts
Each agent needs a system prompt that defines its role, constraints, and output format. The key is specificity.
Bad: "You are a helpful assistant that writes content."
Good: "You are a content writer for a B2B SaaS company. You write blog posts targeting startup founders. Your tone is direct and practical, never salesy. Every post follows this structure: hook, problem, solution, step-by-step, CTA. Keep paragraphs under 4 lines."
The more specific the prompt, the more consistent the output.
Step 3: Choose the right model for each agent
Not every agent needs the most powerful model. Match the model to the task:
- Research and data extraction - efficient models work well (GPT-4o Mini, Gemini Flash)
- Writing and analysis - balanced models for quality output (GPT-4o, Claude Sonnet)
- Complex reasoning - performance models for difficult tasks (Claude Sonnet 4, Gemini Pro)
This keeps costs down without sacrificing quality where it matters.
Step 4: Connect integrations
Agents need access to the tools where work happens. Common connections:
- Slack - for notifications and team communication
- GitHub - for code-related tasks
- Notion - for documentation and knowledge bases
- Google Workspace - for docs, sheets, and email
Each agent should only have access to the integrations it needs. A research agent does not need write access to your GitHub repository.
Step 5: Set up the orchestration
The orchestrator is what makes a group of agents into a team. It decides:
- Which agent handles each task
- How to break complex tasks into subtasks
- What order tasks should execute in
- What to do when a dependency completes
You configure this at the project level with orchestration rules. For example: "Always assign research tasks to the Research Agent. Writing tasks go to the Content Writer. The Content Writer should not start until the Research Agent completes."
Step 6: Test with a single workflow
Do not try to automate everything at once. Pick one workflow, create a task, and watch the agents execute. Review the output, adjust the system prompts, and iterate.
Common first workflows:
- Weekly competitor research report
- Blog post creation pipeline
- Customer feedback summarization
- Documentation updates from code changes
Step 7: Add schedules and triggers
Once the workflow runs well manually, automate the trigger:
- Schedules for recurring work (weekly reports, daily monitoring)
- Event triggers for reactive work (new GitHub issue, webhook from your app)
The agents now run autonomously. You review and approve the output.
Common mistakes
- Too many agents - start with 2-3, add more as needed
- Vague system prompts - specificity beats generality
- Wrong model selection - do not use expensive models for simple tasks
- No approval flow - always review agent output until you trust the workflow