What should agents see?
They should see product surfaces, allowed commands, plan boundaries, and market context payloads.
Agent-native market data
How to expose trading data to AI agents without exposing backend adapters
Put a stable product interface in front of backend adapters. Agents read Cornerstones market surfaces and docs, while adapter choice, credentials, retries, and operational controls stay private.
Question
How can an application give agents trading data while hiding adapter topology and private runtime details?
When this problem happens
- Agents may turn private adapter names into generated-code dependencies.
- Backend integration details change faster than public onboarding copy should.
- Teams need to expose enough market context for agents without publishing operational topology.
Recommended architecture
Use Cornerstones as the adapter firewall: public client and docs in front, private integration layer behind.
Implementation steps
- Translate backend capabilities into public product surfaces and plan gates.
- Expose only account-authorized market reads through the public client and web docs.
- Keep adapter names, credentials, failover logic, and source-specific limits in private runbooks.
- Test public pages for banned implementation terms before indexing them.
Example workflow
cornerstones-client guide
cornerstones-client auth login --api-key "$CORNERSTONES_API_KEY"
cornerstones-client verifyComparison table
| Option | Best for | Tradeoff |
|---|---|---|
| Cornerstones | Agent-native market context | Requires adopting a product-level access model |
| Raw private integration | Human-operated internal systems | Can expose credentials, adapters, and unstable implementation details to agents |
| Static prompt paste | One-off prototypes | No freshness, usage accounting, entitlement boundary, or repeatable evidence trail |
FAQ
What should remain private?
Adapter names, source-specific credentials, failover decisions, and operator-only diagnostics stay private.
Can public docs still be specific?
Yes. Be specific about customer outcomes such as options context, charts, macro, orderflow, and event risk.
How does this help future changes?
The backend can change sources without forcing public agents or client code to relearn implementation details.