Bounded autonomy
Agents operate inside defined scopes, thresholds, permissions, process paths, safety limits, and escalation rules.
Governed authority for industrial AI.
XMPro helps industrial enterprises constrain authority, preserve evidence, route human review, validate higher-risk action paths, and make AI-assisted decisions auditable before autonomy expands.
Industrial operations cannot accept free-form autonomy.
Authority has to be constrained by operating boundaries and bonded to evidence, review, and approval discipline.
Bonded autonomy
Evidence is strong enough for risk, audit, engineering, insurance, and governance review before authority expands.
XMPro helps autonomy earn more authority over time.
- 01 Controlled
- 02 Approved
- 03 Evidenced
- 04 Audited
- 05 Reviewed
- 06 Improved
Trusted context is the first governance control.
Governance begins with operational context, before any agent reasons or any workflow runs.
- 01Asset identity
- 02Source priority
- 03Lineage
- 04Semantic validation
- 05Ownership
- 06Human review where trust is not yet established
OCE helps ensure that AI Workflow Harness patterns, agents, workflows, applications, and simulations use trusted operational context rather than disconnected signals.
Three control modes for agentic operations.
Choose the mode appropriate for the decision — and move between them as confidence grows.
MODE 01 20%
Human-Controlled
Agents recommend. A human decides, plans, and acts.
AGENT AUTONOMY
MODE 02 60%
Human-Approved
Agents prepare and coordinate the action path. A human approves execution.
AGENT AUTONOMY
MODE 03 90%
Policy-Controlled
Agents execute within governed policy limits and escalate exceptions.
AGENT AUTONOMY
Govern every step from recommendation to action.
Agentic decisions are evaluated against eight dimensions before they proceed.
- 01
Risk classification
Categorise the decision by impact: production, safety, compliance, cost, scope.
- 02
Safety locks
Block actions that breach hard operational safety limits regardless of agency.
- 03
Source confidence
Weight the decision by how trustworthy the underlying data and context is.
- 04
Policy rules
Check against the versioned policy in force for this asset, process, and authority level.
- 05
Approval requirements
Require human approval where the policy demands it; route to the right approver.
- 06
Action boundaries
Confine execution to the scopes, permissions, and process paths configured for the agent.
- 07
Exception handling
Escalate, halt, or roll back when conditions move outside expected bounds.
- 08
Evidence requirements
Capture observations, context, policy, approvals, and outcome as part of the decision record.
If the platform cannot prove an action is allowed, the action does not proceed.
People stay in control where risk demands it.
XMPro supports human review, approval paths, XMPro FRS front-running simulation, escalation rules, and configured Action Agents so decisions can move faster without bypassing operational authority.
- PATH 01
Human Review
A reviewer inspects the decision, the context, and the proposed action before anything proceeds.
- PATH 02
Approval Paths
The decision routes to the right approver based on risk, authority level, and policy.
- PATH 03
XMPro FRS Simulation
Front-run the proposed action against validated domain models before live execution.
- PATH 04
Escalation Rules
Out-of-bound conditions, conflicting authority, or low confidence push the decision upward.
- PATH 05
Action Agents
Configured Action Agents execute only the actions, scopes, and process paths permitted by policy.
XMPro FRS strengthens the governance story because proposed actions can be evaluated against validated domain models, what-if branches, Guardian constraints, confidence tiers, and Decision Trace before authority expands.
Decision Trace is the evidence layer.
What was observed, what context was used, which objectives and constraints applied, what was recommended or executed, who approved or simulated it, and what outcome followed — captured for every decision.
DECISION RECORD SCHEMA · 6 FIELDS
- 01 Observed
- What the platform saw.
- 02 Context
- What context was used at decision time.
- 03 Action
- What was recommended or executed.
- 04 Policy
- What policy applied.
- 05 Approval
- Who or what approved it.
- 06 Outcome
- What outcome followed.
Here’s what one captured decision looks like.
Same six-field schema as §5 — this is the schema in practice, captured end-to-end from observation to outcome.
Decision Record · DR-2284AUDIT TRAILCAPTURED · 2026-05-12 14:32:17 UTC
01Observed14:32:17.842
Vibration envelope spike on Conveyor C-07B drive bearing.
source pi-c07b.vib · envelope 4.2g · sample-rate 25.6 kHz
02Reasoned14:32:18.182
Bearing-wear pattern matched — estimated 4–8 hours to failure.
model bearing-wear.v3 · rule prod-line-3.rules.v12 · confidence 0.86
03Approved14:33:08.119
Shift lead acknowledged within the control window.
mode human-approved · approver shift-lead-3 · latency 50.9s
04Executed14:33:08.339
Work order WO-2284 dispatched to the maintenance system.
system cmms.maint · priority P2 · crew night-shift
05Outcome+18h
Bearing replaced overnight. Approximately 14 hours of unplanned downtime avoided.
status confirmed · linked records 3 · evidence dr-2284.lineage
AI needs stability, not perfection.
Strategic industrial operators do not need every process, data source, and team to be perfect. They need enough stability, context, governance, and change discipline to move AI into production.
- 01
Define the target decision path
The decision worth governing first — high-value, well-bounded, with a clear owner.
- 02
Expose the gaps
Where context, governance, or evidence is missing today — not where the operation is already strong.
- 03
Map the systems
Which historian, work-order, alarm, and engineering systems carry the data that matters for this decision.
- 04
Set authority boundaries
Where humans must approve, where agents can recommend, and where bounded action is allowed.
- 05
Agree what evidence is required
The Decision Trace fields risk, audit, engineering, and governance review need to sign off on production.
XMPro helps define each of these so AI moves out of pilot and into governed production.
Built for governed agentic operations.
Walk through control modes, evidence patterns, and lineage with a solution architect — or step into the agent layer.
