The Governance Gap
Factory Agents Are Making Decisions. At Machine Speed.
India's manufacturing sector is targeting $1 trillion in output. PLI schemes across 14 sectors are driving massive investment. AI agents on factory floors are now autonomously controlling quality gates, scheduling maintenance, and optimising production parameters — with cycle times measured in milliseconds, not minutes. When an agent rejects a batch or shuts down a line, nobody's capturing why.
Our Approach
Agents Need Operations, Not Just Uptime
Most vendors ship a model and measure accuracy. Agentic systems on factory floors — where AI reasons, decides, and acts on physical equipment — need a fundamentally different governance architecture.
Factory Agents Without Operations
- Deploy quality agent, validate on test images
- No reasoning trace for rejection decisions
- Maintenance agent runs in a black box
- No central registry of agents across the plant floor
- Compliance documentation done quarterly
- Hope the vision model doesn't drift on a new batch
Factory Agents with Rotavision
- Every agent registered with autonomy level and blast radius
- Reasoning capture for every quality rejection — why was this part rejected?
- Predictive maintenance agent explains failure predictions with sensor evidence
- Human-in-the-loop for production line shutdowns
- Continuous drift monitoring for vision agents on changing production batches
- BIS and ISO-compliant audit trails for every agent decision
Where It Matters
Agentic AI for India's Factory Floor
Not generic models — autonomous agents solving the specific problems Indian manufacturers face every shift.
Vision agents now inspect products at line speed — classifying defects, measuring tolerances, and rejecting non-conforming parts faster than any human inspector. In PLI-eligible sectors, compliance requires complete quality documentation for every production batch. When an agent rejects a batch, the rejection reason must be traceable — not buried in a model's logits.
Agent drift is the hidden risk. A vision model trained on one supplier's raw material degrades silently when the supplier changes, when ambient lighting shifts between seasons, or when a new production batch introduces subtle material variations. Without continuous monitoring, the agent's accuracy erodes — and nobody notices until defective products reach customers or PLI audit documentation falls apart.
Guardian monitors vision agents for drift in real time, alerting when inspection accuracy degrades across production batches. Vishwas captures the reasoning behind every rejection decision — traceable, auditable, and ready for BIS and ISO compliance reviews.
India's factories operate in conditions that stress both equipment and models — extreme humidity, pervasive dust, voltage fluctuations, and ambient temperatures that swing 40 degrees between seasons. Predictive maintenance agents ingest vibration data, thermal readings, current draw, and acoustic signatures from IIoT sensors to predict failures before they happen.
But when an agent recommends shutting a production line for maintenance, the plant manager needs more than a probability score. They need to know which sensors triggered the prediction, what failure mode the agent anticipates, and what happens if they defer maintenance by 8 hours. With unplanned downtime costing 5-10% of production capacity annually, the stakes of a wrong call — in either direction — are enormous.
Guardian tracks agent prediction accuracy over time, catching model degradation as equipment ages. Orchestrate enforces human-in-the-loop policies for line shutdown decisions, captures the full reasoning chain, and manages escalation workflows so maintenance decisions are governed, not guessed.
Production optimisation is inherently a multi-agent problem. One agent manages energy consumption to minimise cost during peak tariff hours. Another optimises yield by adjusting process parameters in real time. A third handles scheduling — balancing order priorities, machine availability, and changeover times. These agents must coordinate, not conflict.
For Indian manufacturers, energy costs are a critical factor — industrial electricity tariffs vary dramatically by state and time of day. PLI compliance adds another layer: production documentation must demonstrate that output targets and quality thresholds are met consistently. When a multi-agent system adjusts parameters autonomously, every decision — energy trade-off, yield adjustment, schedule change — must be documented and auditable.
Orchestrate manages multi-agent coordination across OEE parameters, enforcing policies that prevent conflicting optimisations. Dastavez generates production documentation automatically from agent decision trails — PLI-ready, BIS-compliant, and audit-proof.
Solution Package
Smart Factory Agent Governance Accelerator
A combined assessment, platform, and integration package for manufacturers deploying AI agents across quality, maintenance, and production — with PLI audit readiness and ISA-95 alignment built in.
What's Included
Audit agent readiness against ISA-95/Purdue Model levels. Gap analysis across quality, maintenance, and production agents with PLI compliance readiness roadmap.
Orchestrate + AgentOps configured for manufacturing — quality agents, maintenance agents, and production agents registered with safety integrity levels and OT zone boundaries.
Pre-built connectors for SCADA, MES, and ERP systems (SAP, Oracle) where factory agents operate. Governance layer alongside existing industrial automation infrastructure.
Agent decision trails mapped to PLI documentation requirements across 14 sectors. Automated production batch reporting with full agent reasoning capture for DPIIT audit readiness.
Safety classification for every factory agent — from advisory (no physical impact) to autonomous (direct equipment control). Human-in-the-loop enforcement for safety-critical line shutdown decisions.
Platform Stack
India is becoming the world's factory.
The agents running those factories need governance — not just uptime.
