Your First AI Team: A 3-Agent Blueprint

Getting started with AI agents doesn’t require a giant overhaul.

In fact, you can build your first agent team with just three clearly defined roles that integrate seamlessly into your workflows. Here’s how.

Agent 1: The Research Analyst

Use case: Competitive intelligence, summarizing long-form documents, trend reporting.

Integrations: Slack, Confluence, browser plugin or PDF ingestion.

Tasks:

• Answer questions with citations
• Weekly summaries from RSS feeds
• Competitor briefings

Agent 2: The Documentation Assistant

Use case: Keeping internal documentation up to date, formatting SOPs, drafting help content.

Integrations: Notion, Confluence, GitHub Wiki.

Tasks:

• Auto-generate docs from meeting transcripts
• Fix broken links or formatting
• Suggest updates based on product changes

Agent 3: The QA Reviewer

Use case: Reviewing pull requests and feature releases for bugs, edge cases, and inconsistency.

Integrations: GitHub, Jira, CI/CD pipelines.

Tasks:

• Comment on PRs with missing tests
• Flag areas with tech debt or duplication
• Check release notes against actual commits

Connecting the Team

Each agent can operate independently, but the real power comes from orchestrating them:

• Analyst outputs briefings → fed into documentation suggestions
• Documentation updates → passed through QA for verification
• QA outputs → flagged for analyst to monitor future regressions

You now have a repeatable, cross-functional loop - entirely powered by AI.

Roll Out in Stages

You can deploy this team:

1. As sandboxed assistants with read-only access
2. Then move to supervised write access
3. Eventually embed fully into workflows with audit controls

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Ready to try your first agent team? Launch the starter pack.