What It Means to Be AI-Ready: Building for the Agent Era (Part 3)

In Part 1, I talked about why access to data and interoperability is table stakes for the AI-driven future. In Part 2, I dug into governance, access control, and what to ask when buying software.

Now let’s talk about what it means to build for that future. Whether you’re a product leader, CTO, data architect, or founder, there’s a shift happening — and it’s not just about adding AI to your product. AI will need access to the data from your product.

AI agents will serve as teammates, not just tools. Ideally enabling us to do more, but they’ll only be successful if they can access information across systems and workflows.

1. From UX-First to Integration-First

A lot of product orgs have spent years refining their UI/UX — and that still matters. But increasingly, the question is:

Can your product be used without anyone logging in?

Because that’s what agents want. They want to:

• Query your system,
• Execute tasks,
• Pull or push data,
• Trigger workflows,

…without waiting on a human in the loop.

If your product can’t be used in this way, you’re not going to be part of agent-led workflows.

2. AI-Ready = Well-Documented, Predictable, Modular

And this is where something like Model Context Protocol (MCP) starts to matter.

MCP is still early, but it could be a game changer. It aims to standardize how agents interact with systems — making it easier to plug into multiple tools without custom adapters for each one. Think of it like the modern version of what RSS or REST did for earlier web services.

If your product supports MCP (or is aligned with where it’s going), you’re making it easier for agents to:

• Know what context is available,
• Understand how to navigate that context,
• Take structured actions across multiple systems.

It’s still early, but building with MCP — or similar interoperability protocols — in mind is how enable agents.

AI agents can be more effective with:

• Clear, versioned APIs (REST, GraphQL, whatever your flavor is)
• Modular functionality: Agents want to trigger specific actions, not parse UI screens
• Consistent schemas: So they can learn how to navigate without retraining
• Public or programmatic documentation: Yes, AI can read docs — but they need to exist and be clear and available

You don’t need to build an agent. But you do need to make it easy for someone else to.

3. Your Data Layer Is Now a Product

One of the biggest shifts: internal data is no longer just for dashboards. It’s now an interface for automation.

That means you should be thinking:

• Is our data well-structured?
• Is it labeled and permissioned?
• Can it be queried in real-time?

More importantly:

• Can it be consumed by something other than a human analyst?

You don’t have to expose everything. But you do have to design for exposure. Think of it as an API-first mindset, applied to internal data, for use by AI agents.

4. Organizational Implications: Who Owns the Agent Experience?

If agents are going to be users, then someone has to be responsible for their experience. That’s a weird shift.

You might need new roles:

• Agent Experience Lead: Someone who ensures agents have the right access, training data, and logging
• Agent Governance & Risk: Who ensures the agent is only doing what it’s supposed to do?
• Integration Product Manager: Focused not on UI, but on enabling workflows across systems

This might sound like overkill. But it’s exactly how we thought about mobile or API design when those became mainstream. It’s time to treat agents the same way.

5. Agent-First Use Cases to Inspire Product Strategy

Here are a few ways products could start thinking agent-first:

• Scheduling: Let an agent schedule or reschedule meetings directly via your product’s API
• Content Surfacing: Surface personalized insights to agents that support users in real time (support agents, investor tools, etc.)
• Auto-Reconciling: Agents that match line items between your product and a payment or finance system
• Routing & Alerts: Agents that monitor for anomalies and route issues to the right person/system
• Prepare users for meetings and give them notifications and signals that would be hard to find otherwise

What’s key: These aren’t “nice to haves” anymore. They’re how users will expect your tool to fit into their agent-powered world.

Final Thought: Build With Agents in the Room

The most important shift is mindset.

When you’re planning your roadmap, designing a feature, or launching a new product, ask:

• Can an agent use this?
• Can it be integrated easily?
• Is it discoverable?
• Is it secure?

Because in the next wave of software, your users might not be people. They might be bots acting on their behalf.

The tools that win will be the ones that don’t just tolerate that future — they enable it.