Build your self-driving GTM engineRegister
AI Agents

Build your GTM agents here.Or bring your own. Same runtime.

One data model, maintained connectors, retries with backoff, and a trace of every run. Build in the app or in code. Your team runs what you ship without holding an API key.

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Research agent
AI Research Agent
Research, written back to the model
Cargo’s AI Research Agent gathers, verifies, and enriches lead and company data from every connected source, then writes it back to the model every other agent reads.
Find and Validate Contacts with AI
Enriching Profiles with AI Insights
Accelerate Research with Automation
Example:

Instead of manually searching LinkedIn and verifying CRM records, the AI Research Agent finds contact info, checks for duplicates, and enriches the lead profile with company details. Self-driving, and traced.

In a play

Agents as steps in a play

An agent is one step among tools and connectors. Yours or ours,

it runs on the same trigger, retried and traced like the rest.

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Chain them

Research, then qualify, then hand off: three agents in one play, each reading what the last one wrote.

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Give them your stack

Every connector and tool in the workspace is callable by an agent, with the credentials held by the workspace, not the agent.

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Bring your own

An agent you built elsewhere calls the same models, tools, and context over MCP. Cargo is the runtime, not the replacement.

1import { defineAgent } from "@cargo-ai/cdk";
2
3export const sdr = defineAgent("sdr", {
4connector: openai,
5languageModel: "gpt-4o",
6systemPrompt: "Qualify inbound leads.",
7models: [{ ref: contacts, readOnly: true }],
8tools: [enrichCompany],
9});
10
11// cargo-ai cdk deploy

Give your agents a runtime

Bring the agents you have.Start free, deploy in one command.