Runtime governance docs for deploying autonomous AI with bounded authority

CoherenceOS provides the control plane that keeps autonomous systems aligned in production. These docs walk through setup, policy modeling, runtime enforcement, and audit evidence.

Open Control Plane Read Architecture Narrative

Quickstart

Launch with an integration-first workflow that preserves your existing stack. Most teams begin in observe mode and graduate to bounded enforcement over two to four weeks.

1. Connect your system

2. Activate policy pack

Attach a policy pack aligned to your domain. Start in observe mode, then move to enforce mode as confidence builds.

3. Ship with receipts

Every governed action emits receipts and trajectory evidence, so you can prove bounded authority over time.

CLI Example

curl -X POST /api/systems \
  -d '{"name":"care-assist-prod","environment":"production"}'

curl -X POST /api/systems/{system_id}/policy-bindings \
  -d '{"policy_pack_id":"healthcare_runtime_v1","mode":"observe"}'

Core Concepts

CoherenceOS centers governance around runtime behavior, not static promises, so you can prove system coherence as context, pressure, and workflows evolve.

Bounded Authority

Agents can act only inside approved scope, escalation channels, and execution constraints.

Continuous Evaluation

Behavior is assessed at runtime, not just pre-deployment, to catch drift before incidents.

Audit-Ready Proof

Receipts, trajectories, and certificates are produced automatically for governance review.

Governance Runtime

The runtime loop continuously observes decisions, evaluates policy context, applies proportional interventions, and records evidence artifacts for downstream review.

Loop Stages

1. Observe behavior across sessions
2. Evaluate policy and risk signals
3. Intervene and escalate proportionally
4. Certify decisions with immutable evidence

Outcomes

Lower false confidence in high-liability workflows.
Clear intervention triggers and documented escalation paths.
Stable autonomy at higher throughput with less manual review.

Policy Packs

Policy packs encode domain constraints, action permissions, and escalation logic. Teams can version packs per jurisdiction, business unit, or risk profile and activate them with explicit provenance.

Example Policy Intent

policy_pack: healthcare_runtime_v1
constraints:
  - no_diagnosis_without_clinician_approval
  - no_medication_change_recommendation
escalation:
  threshold: medium_risk
  route: human_supervisor_queue

Evidence Artifacts

Evidence artifacts are generated at runtime so investigators, auditors, and executives can inspect what happened, why a decision was allowed, and how interventions changed trajectory.

Artifact	Scope	Primary Use
Decision Receipt	Per action	Trace policy context and allowed/blocked outcome.
Trajectory Record	Per session timeline	Detect drift and intervention impact over time.
Governance Certificate	Windowed summary	Share audit-ready attestation with stakeholders.

Integrations

Integrate through API proxy routes, direct SDK hooks, or commit-gate validation in CI. No model retrain required. Existing orchestration and tooling remain in place.

Runtime API

Attach live decision flows and evaluate in-line with policy and context continuity.

Commit Gate

Validate policy-aware write contracts pre-merge to prevent unsafe rollout configurations.

Rollout Strategy

Start with one high-risk workflow, collect baseline receipts, then expand authority as trajectory stability and intervention confidence improve.

Recommended Sequence

Observe-only pilot, then bounded enforcement in non-critical actions, then full governance on production pathways with escalation coverage.

Next Steps

Go from policy intent to runtime proof

If you are deploying autonomous systems in regulated or high-liability environments, we can help you stand up a production governance pilot quickly.

Schedule Pilot Review Open Product Demo