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agenticOPS
Agentic Industrial Operations.
Domain-fluent agents that reason over live AVEVA PI data, run your workflows, optimize your operation and act within hard guardrails. From dashboards to decisions — people on the loop.
Agentic frameworkAVEVA PI contextWorkflows & automationOptimizationBounded autonomy
What it does
From dashboards to decisions.
Dashboards show what happened. Agents act on it. agenticOPS is the agentic framework on top of autonomousPI and missionGRAPH: agents that perceive through the world model, reason over live PI data and AF context, and act — within hard guardrails, with every decision on the record.
01
Domain-fluent agents
Each agent owns a domain — energy, production, maintenance, fleet, safety — and reads live PI points with Asset Framework context. It knows what a tag means, not just what it reads.
02
Workflows that act
Alarm to work order. Forecast to setpoint proposal. Blocked aisle to re-route. Multi-step workflows across PI, the world model and your systems, with approval gates where you want them.
03
Optimization, simulated first
Energy, throughput and maintenance windows optimized against your own PI history — proposals are scored in simulation before anything moves. REAL → SIM → REAL.
04
Bounded autonomy
Deterministic planning proposes, agents approve or veto with a stated reason, people stay on the loop. Guardrails, drift monitoring and an evidence ledger for every decision.
On screen · 01 / 01
Six agents. One decision cycle.
This swarmCORTEX session is where the agentic framework runs today. The deterministic coach — assignment, routing, reservations — proposes every move for five humanoids, five forklifts and ten staff. Then six agentic personas read the same missionGRAPH beliefs and the same live PI context and rule on the proposal, in one model call per cycle. The Mission Director holds the objective and the priorities of the shift. Spatial Sense checks the plan against measured position — who is where, what is occluded, which aisle is claimed. The Fleet Orchestrator balances load across machines. The Guardian has veto power and uses it: a person in the aisle, a battery below reserve, a forklift on a crossing route, and the plan does not ship. The Memory Steward carries what happened in earlier cycles. The Evidence Verifier writes every decision, with its reason, to the ledger. The live reasoning feed at the bottom right is those agents thinking out loud: batch released, forklifts routed, each humanoid reserving a package through the shared context with no duplicate risk. Domain-fluent agents, hard guardrails, people on the loop — and nothing an agent says moves a machine unless the deterministic plan and the guardrails agree.
- Mission Director · Spatial Sense · Fleet Orchestrator · Guardian · Memory Steward · Evidence Verifier
- Approve, override with a stated reason, or veto
- Deterministic plan proposes; no unverified model output moves a robot
- Agent decisions simulated and labelled on screen
Where it lands
agenticOPS in mission-critical industry.
Oil & gas, process & energy, mining and manufacturing move first — the floors where the data is richest and the stakes are highest.
Oil & gasCompressor trip forecast → maintenance workflow → crew re-routed away from the zone. Integrity, safety and production handled as one agentic loop.
Industry page →
Process & energyEnergy agents optimize boilers, chillers and compressors against tariff and demand — setpoint proposals scored in simulation, approved by people.
Industry page →
MiningVentilation on demand, blast windows, underground fleet dispatch. Agents that plan the shift around what PI says is happening underground.
Industry page →
ManufacturingLine slowdown predicted eight minutes out → fleet re-planned now → OEE held. From alarm to work order without a human copying data between screens.
Industry page →
Measured results
Same floor. Same fleet. One switch.
Measured in simulation, labelled on screen.
Five humanoids, five forklifts, ten staff, 1,000 paired seeds. With missionGRAPH + swarmCORTEX on top of NVIDIA Cosmos 3: throughput up, near-misses down. Method, numbers and the live system after your NDA.
+25.3%
units per hour vs vision-only floor
1,000 paired seeds · simulated
−23.8%
near-misses between machines and people
1,000 paired seeds · simulated
Unlimited
real-time AVEVA PI tags + AF Unified Namespace context on the graph
structure · industrial ontology · live
Sources: swarmCORTEX V9.2 / V10 evidence (paired-seed simulation, p < 5×10⁻⁵); AVEVA PI data plane commit 280420a. Simulation is always labelled simulated. No field claims. Method, numbers and the live system: after your NDA.
Also from Systown
One measured world. Four products.
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