Relevance AI

Relevance AI frames agent building as hiring. You recruit an agent, give it a role and instructions, equip it with tools, and it works alongside your team — with a manager agent coordinating when several are involved.

The model

• Agents have a role, instructions, tools, and memory, and they run continuously rather than per-prompt.
• Tools are reusable building blocks — API calls, prompts, data lookups, code steps — chained visually.
• Multi-agent teams let a manager delegate to specialists, which handles work too broad for a single agent.
• Triggers start agents from a schedule, a webhook, a form, an email, or a Slack message.

Common roles

Prebuilt agents cover the obvious ones — a BDR that researches and drafts outreach, a support agent that answers from your documentation, a research agent that produces briefs — and each can be customised rather than accepted as-is.

Practical notes

Integrations cover the usual CRM, communication, and data platforms, with an API for anything else. Enterprise plans add data residency and compliance controls.

A free tier includes monthly credits; paid plans scale with usage. It is a good fit for operations teams that want agents without a development project attached.