Build the agent
Return data your systems can consume
Give the model an explicit machine-readable output contract instead of leaving the shape implicit.
Give the agent a JSON schema to follow, with downstream validation where a hard contract matters.
What this changes for your team.
An agent can be configured with a JSON output contract and a schema for machine-consumed work. Yekar.AI includes that contract in the model instructions on every turn. Agent replies are currently prompt-directed rather than runtime-validated, so consumers should still parse and validate the response before acting on it.
How it works in practice.
- 01
Select JSON output and provide the schema that describes the fields and types your consumer expects.
- 02
Yekar.AI stores the contract with the agent setup and includes it in the system instructions for each turn.
- 03
Parse and validate the final reply in your consumer, treating refusals, truncation, or an invalid shape as explicit failures.
What you can plan around.
The behaviour you can design against, stated concretely.
The output format and schema live in the agent setup rather than transient browser state.
The current session-agent runtime instructs the model with the schema but does not enforce the final reply in application code.
Provider refusals and malformed or nonconforming output remain possible, so downstream validation is required for a hard guarantee.
Bring one real process
See how Yekar.AI fits the way you work.
Start with a job your team already owns, plus the tools and decisions around it.
Talk to us