Pre-execution control for AI actions
Before AI acts,
make sure
it should.
An AI agent can send a file, approve a payment, or change a system in seconds. The decision to let it act deserves an explicit checkpoint.
See a decision unfoldDecision Integrity & Pre-Execution Control
Send a customer report
to an external partner.
Action: share file Customer data · External recipient
The file stays unsent until approval.
An instruction is not authorization.
01 — Why it matters
A correct answer can
still be the wrong action.
A report can be accurate and still go to the wrong recipient. A payment can be valid and still need approval. A system change can be useful and still exceed an agent’s authority.
Awenara’s focus is the boundary between a proposed action and execution: establish who may act, check the rules that apply, and decide whether to proceed, request review, or stop.
Explore allow, hold, and block02 — Follow the decision
One proposed action.
Three possible outcomes.
Change the circumstances. See why the decision changes.
Share a customer report with an external partner.
In this example, sharing requires a permitted role, an approved partner, and the data owner’s approval.
- 01Role permitted
Authority
Is this agent permitted to share the report?
- 02Partner approved
Policy
Is the recipient an approved external partner?
- 03Approval missing
Consequences
Has the data owner approved this disclosure?
Pause for the data owner.
The recipient is approved, but permission to disclose the customer data is still missing. Keep the file unsent and request review.
Illustrative decision flow, not a live product demo. These example rules show the approach; no files, approvals, or external systems are connected.
03 — Our focus
More capable agents.
More deliberate control.
For teams building AI agents and automated workflows, the key question is what those systems should be allowed to do. Awenara’s direction is explicit control before consequential actions—not just an explanation afterward.
Explore the decision flow