Ombrulla deploys autonomous AI agents that plan, act, verify outcomes, and coordinate across your enterprise systems - with guardrails, approval gates, and immutable audit trails engineered in from day one. The result is AI that does the work, not AI that talks about doing the work.
Agentic AI is goal-driven software that uses autonomous AI agents to execute multi-step work across enterprise systems of record - safely, measurably, and with human control where it matters. Unlike a chatbot that answers questions or an RPA bot that follows a fixed script, an AI agent reasons about a goal, decides which tools to use, executes those actions, verifies the outcome, and re-plans when reality diverges from the original assumption.
The opportunity is real and so is the failure rate. Gartner expects agentic AI to handle 15% of day-to-day work decisions by 2028 and to appear in 33% of enterprise applications - but also warns that more than 40% of agentic AI projects may be cancelled by the end of 2027 when teams chase the hype rather than governed business outcomes. The differentiator is not the model. It is the engineering, governance, and AgentOps discipline that surrounds it.
Agents move work across ERP, EAM, MES, CRM, and ITSM - so ownership does not rot in inboxes, escalations do not slip between systems, and cycle time compresses measurably. Routine cross-system actions that used to require three people and two days complete in minutes, with full audit, while the team focuses on genuine exceptions.
Inventory levels, capacity availability, active permits, downtime risk indicators - agents act on the operational reality that is true right now, not a snapshot someone exported into a spreadsheet yesterday. This eliminates the most common cause of expensive operational decisions: acting on stale data because retrieving live data was too slow.
Not dashboards. Execution. Agents detect drift, open tickets, trigger playbooks, escalate approvals, and drive workflows to closure - continuously, across every shift, on every site. The distinction matters: a dashboard tells you something is wrong; an agent does something about it within the governance and policy boundaries you have defined.
Every agent decision and every system action is logged with full context - input data, policy applied, tools invoked, approvals captured, output produced - built for regulated and safety-critical operations. When the regulator, auditor, or insurer asks what happened on a specific date at a specific time, the answer is precise, complete, and defensible.
Ombrulla agents handle the messy real-world parts of automation: approvals, retries, evidence capture, and exception handling - without the brittle hard-coded workflow logic that makes traditional RPA expensive to maintain. The agent reasons through deviations from the happy path rather than failing the moment reality diverges from the script.
We deploy specialist agents - planner, executor, reviewer, and watchdog - so you do not bet your plant on a single fragile mega-bot. Each agent has a defined role, a constrained scope, and clear escalation paths. The result is a coordinated team where complexity is decomposed across roles rather than concentrated in one brittle component.
Every agent action is controlled by policy, scoped permissions, safety boundaries, and audit logs - critical for regulated industrial operations under frameworks such as ISO 9001, ISO 45001, IEC 62443, NIS2, and sector-specific regulation. Governance is not retrofitted; it is the structural foundation of every deployment.
Every Ombrulla deployment ties directly to hard operational KPIs: cycle time, downtime exposure, backlog reduction, compliance closure rate, and cost per case. We measure baseline at Discovery and report quarterly impact in the same numbers the operations team already lives by - so business value is undeniable, not anecdotal.
Decompose business goals into executable plans, handle real-world constraints, and re-plan when conditions change mid-execution. The agent does not follow a fixed script; it reasons through the problem, anticipates contingencies, and adapts when the world deviates from the original assumption - which is what makes the difference between automation that survives one shift and automation that survives one year.
Typed inputs, validated outputs, error handling, retries, and policy-aware guardrails on every tool the agent can invoke. No "the model decided to call the wrong endpoint with malformed JSON" failures. Every tool is contractually defined, version-controlled, and observable - turning ERP, EAM, MES, and SCADA interactions into predictable, governed building blocks.
Row-level and column-level permissions, sensitive-field masking, complete access logging, and policy-based data access aligned to your existing identity and entitlement model. The agent sees what the user role it represents is permitted to see - nothing more - and every access decision is logged for audit and security review.
Persistent agent state, pause and resume on long-running processes, cross-session continuity, and structured handoffs between agents and humans. Industrial workflows often run for hours, days, or weeks - turnarounds, shutdowns, multi-stage maintenance - and the agent must maintain coherent state across that timeline rather than starting fresh on every prompt.
Configurable approval gates for high-risk steps; straight-through automation for low-risk routine work. The split between what agents do autonomously and what requires human approval is defined by policy, not by the model's confidence - so risk is governed by enterprise rule, not by emergent AI behaviour. Every approval is captured in the audit log.
Structured logs, distributed traces, regression test suites, scenario-based testing, and operational dashboards covering accuracy, latency, cost, and exception rate. AgentOps is to production agents what MLOps is to ML models and what DevOps is to applications - the discipline that keeps the system reliable as it scales and as the world changes.
Allow- and deny-lists for tools and data, budget controls per workflow, scoped permissions per agent role, action constraints, and execution isolation. The agent operates inside a defined safety envelope - and operations leaders can adjust the envelope without code changes as risk tolerance and regulatory requirements evolve.
Kubernetes, VPC, and on-premises deployment options; enterprise-grade secrets management; role-based access control; single sign-on; complete audit logs; private networking; and SDKs that connect cleanly into your existing CI/CD and platform engineering practices. We deploy to where your security architects and platform team can support it - not the other way round.
Duration:
2–4 Weeks
| Capability | Agentic AI | RPA | Chatbot |
|---|---|---|---|
| Goal-Driven Reasoning | Yes - decomposes goals, plans steps, adapts when conditions change | No - follows fixed scripts | No - answers questions, does not execute work |
| Multi-Step Execution Across Systems | Yes - orchestrates tools, APIs, and workflows end-to-end | Limited - brittle when workflows deviate from script | No - conversational interface only |
| Handles Exceptions and Re-Plans | Yes - reasons through deviations, retries, escalates | No - fails when reality diverges from script | No - escalates to human or fails |
| Governance and Audit Trails | Built-in - policy, permissions, approval gates, immutable logs | Add-on - often retrofitted poorly | Minimal - conversation logs, no execution audit |
| Human-in-the-Loop Controls | Configurable - approval gates for high-risk steps | Manual - requires workflow redesign | Always - human executes the work |
| Observability and AgentOps | Production-grade - traces, metrics, regression tests, drift detection | Basic - run logs and error counts | Minimal - conversation analytics |
| Best Use Case | Complex, cross-system workflows requiring reasoning and governance | High-volume, stable, rules-based tasks with no exceptions | Information retrieval, Q&A, conversational support |
