Agentic AI for the Autonomous Enterprise
Ombrulla deploys autonomous AI agents that can plan, act, verify outcomes, and coordinate across your systems with guardrails, approvals, and audit trails baked in.
Ombrulla Offers Agentic AI as a Customised Service
Most “agentic” pitches stop at a demo. We don’t. Ombrulla delivers Agentic AI as a service custom built around your workflows, your systems, and your risk boundaries.
- Use case discovery + KPI definition (so this doesn’t turn into AI theatre)
- Integration + tool layer (APIs, connectors, controlled automations)
- Governance by design (RBAC, policy rules, approval gates, audit logs)
- AgentOps (observability, evaluation, drift control, regression testing)
- Continuous optimisation (measure → harden → expand)
What Is Agentic AI?
Agentic AI is goal driven software that uses AI agents to execute multi step work across systems of record safely, measurably, and with human control where it matters.
Here’s the uncomfortable truth: the hype is real, and so is the failure rate. Gartner expects agentic AI to handle 15% of day to day work decisions by 2028 and show up in 33% of enterprise apps but they also warn that 40%+ of projects may be cancelled by the end of 2027 when teams chase hype instead of governed outcomes.
What Agentic AI Changes in Industrial Operations
Agentic AI is goal driven software that uses AI agents to execute multi step work across systems of record safely, measurably, and with human control where it matters.
Fewer handoffs. Less waiting.
Agents move work across ERP, EAM, MES, CRM, and ITSM so ownership doesn’t rot in inboxes.
Decisions based on live operational context
Inventory, capacity, permits, downtime risk agents act on what’s true right now, not what someone exported yesterday.
Always on monitoring that actually takes action
Not dashboards. Execution: detect drift, open tickets, trigger playbooks, escalate approvals, and drive closure.
Audit trails you can defend
Every decision + system action is logged with context built for regulated, safety critical operations.
Why Leading Enterprises Choose Ombrulla for Agentic AI
End to end workflow automation
Ombrulla agents handle approvals, retries, evidence capture, and exceptions without brittle hard coding.
Multi agent execution that works like a real team
We use a specialist agent planner, operator, reviewer, watch dog so you don’t bet your plant on one fragile “mega bot.”
Industrial grade governance from day one
Every agent action is controlled by policy, scoped permissions, safety boundaries, and audit logs critical for regulated operations.
Measurable outcomes, not “AI adoption”
Deployments tie directly to hard KPIs: cycle time, downtime exposure, backlog reduction, and compliance closure.
How It Works
A clear, governed path from “idea” to “production agent”without breaking your systems of record.
Step 1: Pick one workflow with a scoreboard
Choose something rules heavy, cross system, repetitive, and measurable (cycle time, error rate, downtime exposure).
Step 2: Wrap your systems with safe tools + guardrails
Turn ERP/EAM/MES/CRM/ITSM/SCADA actions into controlled tools via APIs/connectors/scripts, locked down with permissions and policy boundaries.
Step 3: Deploy one agent, then a small team
Start with planner + executor. Add reviewer/watchdog as risk and complexity rise.
Step 4: Measure, iterate, expand
Track cycle time, automation rate, exception rate, accuracy, and human effort. Improve tools, prompts, policies, escalation logic then scale to adjacent workflows.
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Technical Capabilities That Separate Prototypes from Production
If an “agent” can’t re plan, can’t prove what it did, and can’t be constrained then it’s not an agent. It’s a liability.
Advanced reasoning + planning
Decompose goals, handle constraints, re plan when reality changes.
Tool & API orchestration that doesn’t guess
Typed inputs/outputs, error handling, retries, guardrails.
Secure, governed data access
Row/Column permissions, masking, logging, policy based access.
Memory + long running workflows
State, pause/resume, continuity, handoffs.
Human in the loop built in
Gates for high risk steps; straight through automation for low risk work.
AgentOps (observability + drift control)
Logs, traces, regression tests, scenario testing, dashboards.
Safety guardrails + policy enforcement
allow/deny lists, budgets, permissions, action constraints, isolation.
Enterprise deployment reality
Kubernetes/VPC/on prem, secrets management, RBAC, SSO, audit logs, private networking, SDKs.
Use Cases for Agentic AI
Safer, faster decisions across assets
Downtime and non compliance are board level risks. ABB reports unplanned outages can cost the typical business close to $125,000 per hour and that’s before reputational and regulatory fallout. Where agents deliver leverage:
Permit to Work assurance
- Validate prerequisites, detect conflicts, maintain real time PTW status, retain evidence.
Integrity & corrosion workflow automation
- Consolidate inspection history, rank risk, trigger actions before failures.
Production optimization support
- Recommend safe setpoint adjustments using historian + constraints.
Turnaround/shutdown orchestration
- Align jobs, materials, contractors; track critical path; escalate blockers early.
A Practical Path to Deploy Agentic AI in Enterprise Operations
Identify one high value workflow
Start with a process that’s already hurtingStart with a process that’s already hurting: delays, rework, escalations, compliance gaps, or constant firefighting. The best first win is a workflow that’s rules heavy, repetitive, cross system, and easy to measure.
Outcome
A clearly scoped use case with measurable KPIs such as time to complete, error rate, downtime exposure, or cost per case and stakeholders who actually want it fixed.
Faq
Agentic AI uses autonomous AI agents to plan and execute multi step work across enterprise systems, with controls, approvals, and audit trails.
Chatbots respond to prompts; agents execute workflows calling tools/APIs, verifying results, and keeping state until the job is done.
RPA is best for predictable UI clicks; agentic AI handles variable workflows, exceptions, and cross system decisions via APIsmany enterprises use both together.
Yes, in low risk scenarios; for high risk steps (safety, cost, compliance), you add approval gates and escalation rules.
You restrict what agents can do through controlled tools, validated data sources, structured outputs, and automated verification before actions are committed.
It can beif it’s built with policy enforcement, scoped permissions, human in the loop approvals, and complete auditability.
ERP, EAM, MES, SCADA/historians, CRM, ITSM, data platforms/lakehouses, and custom applications via connectors, APIs, or adapters.
Yes these are common patterns, typically via APIs, integration layers, or approved automation endpoints with role based access and logging.
Through scoped permissions, RBAC/SSO, encryption, secrets management, environment isolation, and full audit logs of every action and data access.
Yesmany industrial enterprises choose on prem/VPC deployment to meet data residency, security, and latency requirements.
AgentOps is the operational layer for agents monitoring, testing, evaluations, traces, rollback/replay, and drift control so reliability improves over time instead of degrading.
Maintenance dispatch, work order automation, quality containment, scheduling re optimization, incident triage, and cross plant performance operations.
Permit to work coordination, integrity workflows, shutdown/turnaround orchestration, production support, and reliability/incident response automation.
A focused workflow can go live in weeks if tools, permissions, and KPIs are defined; a broader scale comes after proving repeatable value.
They fail when teams over scope, skip governance, rely on uncontrolled actions, or can’t prove KPI impact quickly production needs discipline, not demos.