Enterprise AI governance before production exposure

Are your Agents under control?

Building agents is easy. Governance decisions are not.

CARE shows which agents lack adequate control, where accountability is unclear, and what must be fixed before they scale.

  • Before investing heavily into building agents.
  • Before wasting months arguing about accountability.
  • Before it breaks your business.
  • Stop building agents that should never go live
  • Set clear ownership and accountability structures
  • Stay in control when the unexpected happens

Agent portfolio dashboard

Governance coverage

OPTIMIZATION OPPORTUNITY — extend governance with intent

50%

From instance maturity assessment (0–5 scale as % of optimizing)

Governance gaps
1
Risk level exceeds maturity level (both scored)
Agents total
2

Where you need to intervene now

Where harm can show up — before you go deeper.

High risk process

Customer Service (CSR), Refunds & Returns

  • Max structural risk level: 5 / 5
  • 1 active agent(s) in this process
  • Assign an owner. Define the intervention path.

CARE controls agentic risk by providing clarity on accountability, insights into viability, contingency plans, and traceable regulatory evidence, based on structured evaluation of ownership, risk, and control readiness.

Control high-risk agents Ensure regulatory compliance Manage your agent portfolio

How ungoverned Agents fail

Keep your company out of the headlines.

When Agents break governance, failure quickly becomes expensive, public and difficult to contain.

Legal impact

Lawsuit over a Chatbot

One wrong answer. Real liability.

Bankrupt over false pricing

Chatbot invents a discount

One fake price. Real fallout.

Deal or no deal

Dealership agent goes off-script

The Agent got fooled.

Invitation to abuse

Users learn how to game the bot

One exploit teaches the crowd.

Stop signal ignored

When 'No' doesn't mean 'No'

Agreements are not constraints - they're options.

Invitation to harm

A mouthful to chew on

Could your agent trigger irreversible harm?

Governance layers: Accountability, Operations, Functionality
The Accountability gap

Agents outpace accountability

Agents decide automatically, across systems and teams, without a clear line of ownership.

When something goes wrong, the question isn't what happened. It's who owns it?

CARE makes ownership explicit across decisions, consequences and exposure. Accountability is never left to interpretation.

COIL cycle: Classify, Own, Intervene, Learn
Proper CARE

How to achieve Systematic Agent Control?

Agents aren't safe by default. To turn potential into performance, you need control. Agentic risks must be classified, accountability owned, interventions enforced, and learning built into operations.

That's the COIL cycle: Classify. Own. Intervene. Learn.
It's the foundation of Agentic Control.

And CARE is the COIL for your business.

Plans & pricing

CARE grows with your Agentic Strategy.

  • Free — see the problem
  • Starter — structure decisions
  • Pro — run governance
  • Control — stay in control when it matters
  • Enterprise — scale governance across the organization

CARE helps you decide what agents are allowed to exist—and keeps them under control once they do. Progress from diagnostic visibility (Free) through governed proposals (Starter) and operational governance (Pro), to the Control layer for business-critical agents, and Enterprise for deep integration, rollout, and advisory.

What CARE gives you

A governance system for deciding what is allowed to exist.

Governance by Design

CARE embeds governance into agent development - so that agents go live with ownership, viability and risk already resolved.

Make ownership explicit

Process, product, engineering, operations and compliance do not carry the same responsibilities. CARE makes the actual accountability model visible before anything ships.

Define control and recovery

CARE defines what happens when things go wrong: detection, escalation, containment and recovery. Because when it happens, that's the last thing you want to discover by trial and error.

How CARE works

From enthusiasm to governed execution.

1

Classify the agent

Start with intended delegation, process criticality, business impact and risk class. CARE makes the proposal legible before teams disappear into tooling and demos.

2

Test governance viability

Assess ownership, evidence, controls, operational readiness and intervention paths. CARE shows whether the organization can actually govern this agent in production.

3

Approve, constrain or stop

CARE turns findings into a clear decision: proceed, proceed with conditions, redesign, or do not build. Governance stays ahead of delivery instead of behind failure.

When it counts

When do you need CARE?

When agents start affecting real outcomes

Decisions touch customers, revenue, suppliers, or business-critical processes. If a failure would escape the lab, you need explicit risk class, ownership, and controls—not another demo.

When accountability becomes unclear

Ownership is argued after the fact, or no one can show who approved what under which assumptions. CARE makes the accountability model explicit before exposure spreads.

When governance discussions slow execution

Legal, risk, and engineering revisit the same questions without a durable decision record. CARE turns governance into a repeatable path so teams can ship with defensible boundaries.

When regulatory or internal scrutiny increases

Policies tighten, audits multiply, or regulators expect evidence of human oversight and control. CARE gives you consistent documentation and rationale across the agent portfolio.

CARE for accountability

Why you should CARE

Because building agents is easy.

Someone still owns the consequences.

Because technical controls don't remove accountability.

And accountability is what gets tested.

Because agentic failure scales rapidly.

And someone has to answer for it.

Risk scales faster than control

Start Agentic Governance today

Governance is the key to successful AI adoption. Use CARE as the entry point into serious AI governance. Start with the risk class, or talk through where your current pilot approach fails to create enterprise-grade control.