Ombrulla deploys governed agentic AI agents that plan, execute multi-step workflows across ERP, EAM, MES, SCADA, and ITSM, verify outcomes, and produce immutable audit trails, with human-in-the-loop controls for every high-consequence decision.
Gartner, August 2025
Gartner, 2025
Deloitte, 2025
Gartner, 2025
Deloitte Emerging Technology Trends, 2025
MuleSoft / Deloitte Digital, 2025
Gartner, June 2025
Receives goals and manages end-to-end execution.
Receives the goal from a human trigger, a system event, or a PETRAN operational alert. Decomposes the goal into a sequence of executable steps. Marshals the available tools and Specialist agents. Manages the overall workflow state, handles exceptions, re-plans when steps fail, and decides when to escalate to human approvers. Analogous to IBM’s ‘conductor’ model, an LLM-powered supervisory agent.
Every engagement begins with a workflow assessment: identify the process that is already hurting, agree what ‘good’ looks like in measurable terms (cycle time, error rate, backlog reduction), and define who owns the outcome. This step prevents the most common failure: agents built for a process nobody was accountable for improving.
Ombrulla wraps every enterprise system (SAP, Oracle, Maximo, ServiceNow, SCADA, historians) in controlled tool definitions: typed inputs/outputs, permission boundaries, error handling, retries, and audit logging. No agent can improvise how a system is called, it uses a tested, governed tool.
RBAC, policy rule engine, human-in-the-loop approval gates for high-consequence decisions (permit issuance, shutdown sequences, supplier payments), and immutable audit trails. Governance is configured before any agent acts in production. Compliant with IEC 62443, ISO 55000, OSHA, PSSR, GDPR.
Observability dashboards, agent action logs, regression test suite, drift monitoring, cost governance, and scenario testing are implemented as part of the production deployment. Agents do not behave differently after three months of operation without anyone noticing.
Every production metric (cycle time improvement, automation rate, exception rate, accuracy, human effort saved) feeds a structured improvement cycle. Agents are extended to adjacent workflows only after proving ROI on the initial use case.
Ombrulla’s Orchestrator agents use LLM-driven reasoning to break high-level goals into executable steps, select the right tools and agents, manage dependencies, and re-plan when steps fail, ensuring workflows continue even when conditions change.
Every enterprise system action is wrapped in a tool with typed inputs/outputs, validation, error handling, retries, and permission controls. Agents interact only through these tools, ensuring reliable, structured system calls and preventing unpredictable API behavior.
Agents access only permitted data via tool-level controls enforced at the data layer. Row-level security restricts access by asset, column masking protects sensitive fields, and all queries are fully logged with identity, parameters, and timestamps.
Industrial agentic workflows span days or weeks, requiring persistent state across shifts and systems. Ombrulla maintains this using structured storage, ensuring continuity despite context resets, outages, or handovers.
Not all agent actions are fully autonomous. Actions are classified by risk: low-risk tasks run automatically, medium-risk actions include logging and review, and high-risk actions require explicit human approval before execution.
AgentOps is the production operations layer for agentic AI. Without it, agents degrade over time due to data shifts, API changes, and model updates. Ombrulla includes AgentOps as a standard component in every deployment, not an optional add-on.
Guardrails prevent agents from operating outside defined boundaries. They include input validation, action allow/deny rules, output checks for compliance, and circuit breakers that halt operations if errors, costs, or anomalies exceed limits.
Agentic AI must deploy within enterprise-grade security architecture. Ombrulla supports Kubernetes-based deployments (cloud or on-prem), VPC/private cloud setups, private model hosting, secure secrets management, SSO integration, network segmentation, and full audit logging to SIEM systems.
| Dimension | Traditional RPA | Chatbot / Copilot | Ombrulla Agentic AI |
|---|---|---|---|
| Handles novel situations | No: fails when reality deviates from the programmed script | No: generates suggestions or answers; does not execute actions | Yes: executes single fixed scripts; no reasoning or re-planning |
| Multi-system coordination | Limited: typically one integration target per workflow | No: chat-only; no native system integration | Yes: linear process only; no dynamic routing |
| Adapts when steps fail | No: hard failure or silent skip | Not applicable: suggestion only | Yes: errors cause workflow abandonment |
| Governance & auditability | None native: custom logging required per workflow | Limited: conversation logs; no action audit trail | Yes: limited: process logs, limited action evidence |
| Works in legacy environments | Limited: requires modern APIs on all target systems | Depends on integration; often separate interface | Yes: strong: works with legacy systems via connectors |
| Handles exceptions & escalation | No: exceptions require human re-activation | Sometimes: can route to human, but as chat suggestion | Yes: limited: escalation requires separate workflow configuration |
| Learning over time | No: static rules only | Yes: LLM improves with prompting; limited memory | Yes: no: rule logic is static |
| Cost model | Low per-run; high for complex process design | Per-message or per-seat; growing LLM costs | Per-process or per-user; licensing model |