01
Go ahead
The decision fits the mandate and constraints. The agent may continue.
Ala independently reviews what an AI agent wants to do — before it does it. Mandates stay with your organization. Agents keep working. Control stays human.
Why Ala exists
AI agents are moving past suggestions. They call tools, change records, trigger workflows and take actions that affect customers, money and operations.
Logs and dashboards explain what already happened. Access controls may confirm that an agent can reach a tool — but not whether this decision is appropriate under your rules, in this moment, for this context.
Ala sits between the agent's intent and real-world execution: an independent review of the proposed decision, every time it matters.
How Ala responds
Each proposed action is checked against declared intent, policy and live constraints. Ala returns a clear operational outcome — calm, consistent and explainable.
01
The decision fits the mandate and constraints. The agent may continue.
02
The decision sits outside authority — wrong scope, party, or policy. It does not proceed.
03
Direction is fine; scale is not. Ala returns a constrained version that can still run.
04
Judgment is needed. The decision waits for a named human before anything continues.
How Ala works
You define what agents are trusted to decide. Ala reviews each consequential proposal against that trust — independently, before execution.
See which decisions agents attempt, without changing outcomes yet.
Objectives, limits, approved parties and operating boundaries.
Ala evaluates the exact proposed decision against policy and context.
Go ahead, hold back, narrow it, or ask a person — with a clear reason.
Every review and human judgment is kept for later assurance.
Agents propose decisions. Ala reviews them. People keep the exceptions.
Routine decisions inside the mandate keep moving. Edge cases return to human judgment — with a full trail of why.
What you get
Where it fits
Review proposed trades against mandates, risk limits and approved instruments before they become live.
Govern agent-initiated payments and transfers before funds leave the institution.
Keep AI-assisted workflows aligned with policy as they touch customers and ledgers.
Review purchase orders, supplier actions and vendor payments before they execute.
Govern claim settlements, refunds and other consequential operational decisions.
Supervise agents working in ERP, CRM and HR systems before they change real records.
In practice
An agent proposes a payment to an approved vendor, within the spending mandate. Work continues.
The request is valid in kind but too large. Ala returns a reduced version that stays inside the limit.
A transfer points to an unknown counterparty. The decision does not reach execution.
Something needs verification or judgment. A named reviewer decides — and that decision is recorded.
How organizations adopt Ala
Deploy beside the agents you already run. Move from visibility to binding governance without rebuilding integrations at every stage.
01 · Observe
Map the decisions agents attempt. Keep the record. Change nothing yet.
02 · Shadow
See what Ala would have decided while your current process still runs.
03 · Enforce
Ala's responses count on a defined scope. Widen as confidence grows.
04 · Autonomy
Routine decisions inside the mandate flow; exceptions stay with people.
Next step
Show us one place where an agent already takes a consequential decision. We'll map how independent review would sit — observe first, govern when you're ready.