Designing a Legal Research API for AI Agents

An autonomous client should not infer product behavior from visual pages. It needs a versioned API with explicit authentication, bounded input schemas, stable output fields, and machine-readable capability discovery.

The API should state what it can do and what it refuses to do. Outcome prediction, invented authority, and silent fallback are not acceptable substitutions for missing research data.

A single free-text prompt hides distinctions that matter to legal analysis. Separate fields for jurisdiction, matter type, parties, chronology, evidence, issues, and requested outcome allow retrieval to form better queries and allow downstream agents to reason over explicit facts.

Structure also reduces accidental scope expansion. An agent can update an evidence item without rewriting the entire matter narrative or changing the user’s requested relief.

Agent responses should include source type, citation, title, excerpt, canonical URL, and verification state. Consumers can then present the research, perform additional validation, or reject material that does not satisfy their trust policy.

This is the foundation for multi-agent work: one agent gathers authority, another evaluates factual support, and a human reviewer retains a clear record of what each step used.