Use case · AI adoption
Your AI agents know what they are responsible for
An agent without org context is fast, but it is guessing. roleALPHA gives it a role, a remit and rules — and makes its work traceable.
Grounded·Audited·Governance-ready
Context
Agents without org context act on guesswork
AI agents need exactly what was never structured: a role, a remit, rules.
- 01 No accountability The agent does not know what it is responsible for or where its limits are.
- 02 No records Later nobody can say under which rules the agent acted.
- 03 Unclear value Where an agent truly pays off stays a gut feeling.
Solution
How you ground your agents
Agents become organisation members — with context and guardrails.
- 01 Create as a member The agent gets a purpose, activities and responsibilities in the org model.
- 02 Bind to value streams & policies Roles, value streams and policy guardrails are linked; the context is machine-readable (MCP).
- 03 Operate audited Every context retrieval is logged — solid for the EU AI Act.
Data & control
Data and control stay with you
- EU operation & GDPR Operation in the EU, Austrian law — your data stays within Europe.
- Data sovereignty Domain data optionally in your own cloud or on-premise; the control layer stays separate.
- Accountable Access, approvals and agent context are logged — solid for the audit.
FAQ
Frequently asked
Does roleALPHA run the agents?
No. They run in your runtime — roleALPHA grounds and logs them via MCP.
Does roleALPHA enforce the guardrails?
Today guardrails are declared, versioned and audited; enforcement is with the consuming agent.
Where do we see if an agent pays off?
From effort, value streams and objectives — the dedicated radar is emerging, the data is there.
More context
Related
Introduce agents safely.
We'll show how roleALPHA grounds them in your org model.