Authority Governance for AI Agents
Govern what your agents are authorized to do.
Your AI agents already have credentials and permissions. Neither one says what they are allowed to decide. BotAris lets you define that authority, enforce it at runtime, and account for every decision afterward.
Humans define authority. BotAris enforces it.
The problem
"What is this agent allowed to decide?"
AI agents are moving from answering questions to taking actions: approving requests, changing configurations, moving money, and committing the organization. The current security stack governs who the agent is, what it can access, and what entitlements it should have. What is not governed is what that agent is authorized to do with the permission assigned.
An agent with valid credentials and valid permissions can still make a decision it was never authorized to make. The common response is to find the agents first: discover them, register them, assign permissions, review them on a cycle. That sequence assumes you know what is running. Agents spawn other agents. They are created and destroyed inside a single workflow. Any inventory is out of date the moment it is written, and a control that depends on a current inventory fails on precisely the agent you did not know about.
Authority layer
what the agent is allowed to decide
Identity and access layer
who the agent is, what it can reach
Non-registered Agents
Undiscovered and unmanaged
Our position
We do not discover agents. We govern actions.
Discovery-first governance has a ceiling. You cannot register what you have not found, you cannot assign permissions to what you have not registered, and in an environment where agents create other agents the gap between what exists and what you have catalogued only widens.
So we do not build governance on visibility. BotAris evaluates the action itself, in the action path, against the authority model. A consequential action is checked whether the agent behind it was registered last quarter or came into existence four seconds ago.
That works because enforcement sits on the surface, not on the agent. The check lives where the action has to pass: the tool call, the API tier, a gateway in front of the system of record, a sidecar beside the workload. An unregistered agent's action is evaluated because of where the check sits, not because we knew the agent existed. Coverage follows the surfaces you put enforcement in front of, rather than the completeness of your inventory.
Discovery and inventory are still worth doing. They are not what enforcement should depend on.
Authority = Decision Rights + Constraints + Accountability
Decision rights
What an agent is delegated to decide, by whom, and in what scope.
Constraints
The limits, conditions, and thresholds that bound each decision.
Accountability
An attributable, reviewable record of every decision made under delegation.
How it works
Define. Enforce. Account.
Define
Patent pendingWrite the decision rights delegated to each agent, and the constraints on each one, as governed policy. Who delegated it, what it covers, and where it stops.
Enforce
Patent pendingEvery consequential action is evaluated against that authority at runtime, in the agent's action path, before it executes. Each evaluation resolves to allow, constrain, escalate to a human, or deny.
Account
Every decision, delegation, and outcome is written to a tamper-evident ledger built for auditors, regulators, and boards. Evidence of authority, not just a log of actions.
BotAris is the authority governance control plane for AI agents. It sits above identity and access and works with the IAM investments and agent platforms you already run.
The control plane
Five components. One definition of authority.
Authority Model
Decision rights and constraints expressed as governed policy. The source every evaluation is checked against.
Decision Authority Enforcement Point (DÆP)
Patent pendingRuntime evaluation of each proposed action against delegated authority, in the agent's action path. Deployed where your agents already run.
Governed Authority Knowledge Base
Policies, decision taxonomies, authority templates, process constraints, escalation rules, and approved control narratives, versioned and human-approved.
Accountability Ledger
A tamper-evident record of decisions, delegations, and outcomes. Built to answer who authorized an automated decision and on what basis.
Governance Console
Oversight, management, and administration for the people accountable for agent authority.
Every DÆP pattern, from an in-process API call to a reverse proxy in front of an application, evaluates against the same Authority Model and writes to the same ledger. Patent pending
Why now
Agents are entering production
Enterprises are moving agents from pilots into workflows with real financial, operational, and legal consequences.
IAM was built for people
Identity and access categories assume human users and static service accounts, not autonomous software making judgment calls at machine speed.
Accountability is becoming mandatory
Boards, auditors, and emerging AI governance frameworks increasingly require organizations to show who authorized an automated decision and on what basis.
Give every agent a mandate, not just a credential.
BotAris is onboarding a limited group of enterprise design partners. Design partners shape the product roadmap and receive early access. If you are putting agents into workflows with real consequences, we want to talk.
Get in touch