An AI agent is only as good as the tools it can use

The problem: an agent that sees nothing

However good it is, a language model doesn't know your calendar, your campaigns or your CRM. Without access to those tools, the agent answers with generalities, or worse, makes things up. An agent's value doesn't come from the model: it comes from what it can fetch and do on your behalf.

What I built at Twin

I extended Twin's agents with around twenty connections to everyday tools. Each connection gives the agent specific actions it can trigger on its own:

• Google Workspace: read and search emails, documents and calendar events.
• Meta: retrieve ad campaign performance.
• Slack: read channels, find a conversation, post a message.
• HubSpot: look up contacts, companies and deal status.

The rules that make an agent reliable

• Narrow, well-described tools: an agent picks better between “find a deal” and “list all deals” than between two vague tools.
• Filtered responses: the agent gets the useful information, not 500 raw lines that drown the context.
• Least-privilege access: the agent only reaches what it needs, nothing more.
• Explicit errors: a failing tool says why, so the agent can fix its request.

“An agent without tools is a brilliant consultant locked in a windowless room.”

The result

The agents fetch information from the user's tools themselves instead of asking them to copy and paste. That's where AI starts saving real time.