Why data quality matters for agents
An agent may combine search results, API responses and tool outputs before taking action. Even if its reasoning is coherent, a malformed or stale input can produce an unsafe business decision. Treat data quality as an application control rather than a prompt-writing trick.
Validate shape and semantics
Parse JSON strictly, enforce required fields and expected types, and check business invariants such as nonnegative quantities and supported currencies. Schema validation cannot establish whether a claimed balance, inventory count or customer status is factually correct.
Check freshness and provenance
Record the source, observation timestamp and relevant contract version. Reject or downgrade observations older than the workflow's acceptable age. Verify authorization and provenance separately; a schema-valid result from an untrusted tool remains untrusted.
Use deterministic gates for actions
Before writes, purchases or notifications, evaluate explicit policy conditions. Return structured ALLOW, WARN or BLOCK outcomes and enforce them in code. Require human approval for high-impact or uncertain cases. Do not let the model override a failed check by rephrasing the request.
Measure reliability over time
Track validation failure rates, stale observations, blocked actions, retries and false alarms. Review examples of legitimate inputs that were rejected. Tune policies deliberately and version them so changes are auditable.
A practical starting point
Begin with one high-value workflow and its most failure-prone external input. Define a JSON contract, a freshness threshold and an action policy. Zorqelio can supply validation and monitor evidence; see the AI agent use case and MCP guide.
For implementation details and current capabilities, consult the Zorqelio documentation. Technical examples are illustrative and should be tested against your own policies and supported schema dialect.