AI Agents
Grounding for models that would otherwise hallucinate a date
The problem
What is actually going wrong
Language models are confidently wrong about Hindu calendar dates. They interpolate from training data, and festival dates are exactly the kind of fact that moves every year.
Our approach
Use the API as a tool call. Because responses carry their derivation, the agent can cite reasoning rather than asserting a date, and a retrieval layer has something substantive to index.
Workflow
How the integration runs
Define
Register the endpoints as tools from the OpenAPI 3.1 document.
Call
The agent resolves calendar questions by calling rather than recalling.
Ground
The explain trace becomes the citation in the agent’s answer.
Verify
Confidence scores let the agent hedge honestly when a tie-break fired.
Integration
Endpoints and outcomes
Endpoints used
All of these are described in the OpenAPI 3.1 document, so you can generate a typed client rather than hand-writing request code.
What you get
- Eliminates a well-known hallucination class
- Machine-readable reasoning, not just a value
- Spec-driven tool definitions stay in sync with the API
Build it this week
The Free plan covers 500 requests a day, which is more than enough to prototype the workflow above.