Cornerstonesagent-native market context

Agent-native market data

Options market context for AI agents

Use Cornerstones to expose options context as a premium product surface. Agents can reason about chains, walls, analysis context, and event risk while private data sourcing and execution remain hidden.

Question

How can AI agents use options context without direct private market-data integrations?

When this problem happens

Recommended architecture

Expose options as a premium read-only context surface and keep execution, position state, and private adapters outside the public agent boundary.

Implementation steps

  1. Begin with access-matrix discovery so the agent understands options are a gated context surface.
  2. Use authenticated Pro or higher access when options context is required by the workflow.
  3. Keep returned context read-only and separate from position sizing or order execution.
  4. Record which options surfaces were used in review notes for auditability.

Example workflow

cornerstones-client guide --topic options-context
cornerstones-client auth status
cornerstones-client verify

Comparison table

OptionBest forTradeoff
Cornerstones options contextAgent-readable chain and analysis workflowsRequires plan-aware access
Direct private options feedInternal execution systemsToo much source detail for public agents
Static option screenshotsHuman discussionNot machine-verifiable or fresh

FAQ

Is options context available to every user?

No. Options context belongs to plan-aware premium surfaces.

What can agents do with options context?

They can analyze market structure, event risk, and evidence, but not execute trades through Cornerstones.

Why mention plan gates?

Plan gates prevent agents from assuming premium data under starter access.

Should options copy name backend sources?

No. Public pages should discuss product capabilities only.

Related Cornerstones resources

Give your agent market context without leaking infrastructure.

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