How to Make Your Data Agent-Ready

Enterprises are deploying AI agents into workflows that used to need a person. The agents are capable. The data underneath them, usually, is not ready.

“Agent-ready” is a specific bar. It is worth defining precisely, because most data fails it quietly.

What “agent-ready” means

Data is agent-ready when an autonomous agent can discover it, interpret it correctly and trust it — without a human in the loop.

Break that into what the agent actually needs:

• Discoverable. The agent can find the right data product by intent, not by knowing a table name in advance.
• Self-describing. Schema and semantics are machine-readable. The agent knows amount is in cents, not dollars, because the contract says so.
• Trustworthy. Freshness and quality are guaranteed and checkable. The agent can read the SLA, not guess at it.
• Policy-aware. Access rules travel with the data. The agent knows what it may and may not do with each field.

A human analyst supplies all four of these from experience and judgement. An agent has neither. It needs them written down.

Why “good enough for BI” is not good enough for agents

Dashboards are forgiving. A stale number on a dashboard gets noticed at the next review. A human reading a report applies a lifetime of context — they know the quirks, the caveats, the “ignore Q2, the migration broke it.”

An agent applies none of that. It consumes the data literally and acts immediately. Its tolerance for ambiguity is zero. This is why organisations with mature BI still find their AI pilots stalling: the data was good enough for a human to interpret, and not good enough for a machine to.

A five-step path

1. Pick one high-value workflow an agent will run, and identify the data it touches.
2. Define a contract for each of those data products — schema, semantics, freshness, owner, policy.
3. Compile and deploy the contract so the data product is real, governed and discoverable. A compiler such as forge-cli does this from a single file.
4. Expose it to the agent through a discoverable interface, so the agent finds it by intent.
5. Monitor the guarantees. Freshness and quality SLAs are only useful if a breach is caught before the agent acts on it.

Notice this is not “fix all your data.” It is “make the data this agent needs into proper data products.” Agent-readiness is earned one workflow at a time — and that is also the fastest way to show value.