How does Traverse prevent AI agent errors?

Traverse puts three enforcement points between an AI agent and your business logic: schema-based tool definitions at discovery, precondition validation before execution, and postcondition validation after. Each one catches a different class of error before it can propagate.

Accurate specs at discovery

The first source of agent errors is a mismatch between what the agent thinks a tool does and what it actually does. Traverse eliminates this by surfacing the full JSON Schema for every capability at tools/list time. The agent does not build assumptions from a description — it reads the schema. Required fields, types, value ranges: all machine-readable and authoritative.

Precondition enforcement before execution

Even with a good schema, agents sometimes construct inputs that technically match the type but violate business constraints — a quantity of zero, a negative price, a date in the past. Preconditions catch these. The runtime checks them before the WASM binary runs and returns a structured error: which field failed, what value was provided, what the constraint was. The agent can read this and self-correct.

Postcondition enforcement after execution

A buggy capability could return an output that violates the contract. Maybe a pricing function returns a negative total under an edge case. Postconditions catch this before the output reaches the agent. The agent never reasons on top of a result that violates the contract's guarantees.

Structured errors the agent can act on

All three enforcement points return structured errors through the MCP protocol. The agent receives a JSON object with the failure type, the failing field, and the constraint. This is more useful than an HTTP 400 with a message string. A well-designed agent loop can read the error, adjust the call, and retry without human intervention.

See how Traverse handles errors for the full error type reference.