Structured Perception Interface
Transform raw environmental input into structured representations before the reasoning layer sees them.
This page is the complete instruction page for one pattern called "Structured Perception Interface." It explains the problem this pattern solves, the idea behind the solution, when you should (and should not) use it, and what happens afterward — both the good effects and the costs.
This matters because building AI agents is not just about making them clever. It is also about making them safe and predictable. Following a well-tested pattern like this one helps avoid common mistakes, and shows you exactly which safety rules and regulations it connects to, listed under "Standards Mesh" on this page.
Context
Agents receive text, tool outputs, events, documents, images. Feeding raw heterogeneous payloads directly into the model wastes context window, leaks sensitive fields, and makes reasoning brittle.
Problem
Unstructured perception makes downstream reasoning unpredictable and expensive.
Forces
- Fidelity of raw input vs clarity of structured representation
- Flexibility vs schema discipline
Solution
Define a perception layer that performs extraction, entity recognition, and semantic interpretation, producing a typed observation object for the reasoning layer. Strip or mask sensitive fields at this boundary.
Applicability
- Any agent processing documents, sensor data, or mixed inputs
- Multi-modal agents
Anti-Patterns
- Raw tool output concatenated into the prompt
- Sensitive fields entering reasoning context unnecessarily
Consequences
- +Predictable, cheaper reasoning
- +Clean boundary for privacy controls
- −Schema maintenance
- −Can drop signal the schema did not anticipate