What DaemonCore is
The governance and control layer for autonomous AI agents.
Agents are becoming able to act on their own. Instructions alone are not authority. Orchestration decides what to do; governance decides whether it is allowed.
DaemonCore is the external governance layer that sits around autonomous agents — establishing authority, enforcing boundaries and preserving evidence. It does not make agents do anything. It determines what they may do, records available execution evidence for inspection.
What it provides
Authority
Agents do not grant themselves authority. Identity and permissions are established externally — verified, not self-asserted.
Boundaries
Permissions and execution constraints are external to model intent. The deployment must connect those constraints to its execution paths.
Governed communication
Agent-to-agent work moves through defined messaging roles and schema. Messages that fail validation do not route.
Execution decisions
Proposed work reaches an explicit admission point. The governance gate decides before the action proceeds — admit or reject.
Evidence
Captured runtime events provide an inspectable record: what was requested, by whom, what was decided, and why. Traceable to context and authority.
Where it sits
DaemonCore is not inside the agent and not above it. It is the boundary around it. Orchestration frameworks coordinate work. Models produce output. DaemonCore governs what is permitted to happen and records what did happen — a separate concern from both.
A setting that can be overridden by the thing it governs is only advice. Governance that lives inside the agent is, at best, a suggestion.
What this enables
- Trust agent behaviour — boundaries are enforced, not hoped for
- Multi-agent governance — multiple agents, governed roles, controlled paths, single boundary
- Vendor independence — different AI providers under unified governance with separate runtime attribution
- Auditable operations — every action traces to defined authority, available for inspection after the fact
- Evaluate multi-agent risk — inspect each actor, assignment and tool boundary as the system grows