What makes a company AI-native?

How is AI-native different from adding AI features?

Many companies add AI to an existing product, such as a chatbot in the help center or a summary button. That can be useful, but the company still works the way it did before.

An AI-native company is organized differently. Its team is small, and agents handle routine engineering, testing, routine support replies, research and project tracking. People spend their time on judgment: what to build, what to ship and what to approve.

Why should products be usable by agents?

More and more work is started by an agent acting for a person. A developer asks a coding agent to set up a test environment, or a founder asks an assistant to find creators for a launch. If a product can only be used by clicking through a website, the agent gets stuck.

AI-native products expose the same capabilities through an API, a command-line tool and an MCP server, the Model Context Protocol that lets assistants such as Claude call tools directly. Both North Bay Ventures companies work this way. Sandboxer's MCP server lets an agent create and control sandboxes, and Transformer's MCP tools let an agent run a creator campaign.

Where do people stay in control?

Being AI-native doesn't mean letting agents do anything. The useful split is between reversible, routine work, which agents can do freely, and decisions with real consequences, which need a person. At North Bay Ventures that means a person approves anything that spends money, changes a customer's plan, deletes data, changes legal terms or DNS, promises a date, emails customers in bulk or carries risk in production. Routine support replies go out without waiting, with a person copied.

Products can give their users the same guardrails, for example by keeping a person in control of spending and approvals even when an agent runs the day-to-day work.

What new infrastructure do AI-native companies need?

Agents need safe places to run code. Code written by an AI, or downloaded from an unknown source, shouldn't run on a laptop or a production server. That's the problem Sandboxer solves: every agent gets its own isolated microVM that can be created, paused, forked and thrown away in seconds.

Agents also need ways to reach people. When building is cheap, distribution becomes the hard part. Transformer lets a builder, or the builder's agent, pay creators to reach their audiences, and pay only for verified posts.

And agents need a safe way to ship. When agents merge many small changes, each one should reach production switched off and be turned on deliberately. Flagger.gg lets agents create, roll out and kill feature flags through MCP, a CLI and SDKs, with an audit trail, and lets a team require a person's approval for wider rollouts when it wants one. See how the three work together.

How do you start building an AI-native company?

• Start small and give agents real, well-scoped work from day one.
• Write things down, such as runbooks, rules and decisions, so agents and people work from the same instructions.
• Make every action safe to retry and easy to roll back.
• Decide in advance which actions need a person's approval.
• Build an API and an MCP server alongside the website, not after it.