AI Operations (AIOps)
Managed AI Services: Operating AI Solutions and Agent Systems Securely, Auditably, and Cost-Effectively
AIOps: From a Successful Pilot to Stable Production Operations
"A successful pilot project demonstrates the professional and technical feasibility. For long-term implementation, a robust operational model is also needed that establishes binding guidelines for accountability, service quality, and further development."
AIOps bridges this gap. We handle the production operation of analytical ML models and solutions, from GenAI applications to individual agents and multi-agent systems. To achieve this, we combine specialized MLOps, LLMOps, and AgentOps practices with established managed services processes.
The result: reliable operations, controlled quality and risks, transparent cost-effectiveness, and a scalable operational structure.
Why AI Operations Is Now Becoming a Discipline in Its Own Right
The Growing AI Landscape
With every productive AI application, the number of models, data sources, agents, and platform dependencies increases. AIOps establishes the standardized processes and responsibilities needed to reliably operate this growing portfolio.
Quality and Reliability
Models drift, data sources change, and prompts need to be refined. Continuous monitoring and defined evaluation cycles ensure the quality of results during ongoing operations.
Safety and Control
Agents access systems, use tools, and trigger actions. Identities, permissions, guardrails, and escalation paths limit autonomous behavior.
Costs and Cost-Effectiveness
Infrastructure, token consumption, and model selection determine the operating costs. Transparency on a per-application basis and active optimization enable cost-effective management.
Governance and Compliance
Logging, human oversight ("human-in-the-loop"), and continuous risk monitoring must be integrated into operational processes. This ensures verifiable evidence, including for regulatory requirements.
A Common Foundation – Specialized Operational Disciplines
All AI systems share a common operational foundation consisting of service management, observability, security, cost control, and governance.
Depending on the type of system, it is supplemented by specialized operational disciplines:
- MLOps for traditional ML systems – manages data and model lifecycles with versioning, controlled deployments, drift detection, and retraining
- LLMOps for GenAI Applications – extends operations to include the management of prompts, configurations, and retrieval components, as well as evaluation, hallucination control, latency monitoring, and token costs
- AgentOps for agent-based systems – complements the control of actions, identities, permissions, guardrails, tool access, and decision paths for individual agents and multi-agent systems
AIOps in Practice
Initial Situation: A GenAI application has successfully completed its pilot phase. The project team is needed for new initiatives, and there is no permanent operations team. Quality, availability, and costs are only checked sporadically.
With AIOps: After a structured onboarding process, the operations team takes over day-to-day operations, including monitoring and incident management. Defined evaluations assess the quality of results, costs are transparently allocated to the application, and optimization measures are implemented in a controlled manner. The internal team remains focused on technical development.
Initial Situation: Multiple agents coordinate a business process across system boundaries, use different tools, and initiate actions independently. Authorizations have evolved over time; decision-making paths can only be reconstructed to a limited extent.
With AIOps: Each agent is assigned a unique identity with defined permissions and allowed actions. Tool access and decision-making processes are logged, and guardrails are configured and implemented. Anomalies are escalated based on defined thresholds. Critical decisions remain subject to human approval.
Initial Situation: Several departments operate AI solutions on different platforms. Quality standards, approval processes, and cost models vary; there is no consolidated view of performance, risks, and cost-effectiveness.
With AIOps: A unified operational model standardizes monitoring, quality measurement, cost control, and policy enforcement across all solutions. Key metrics are reported both by application and for the entire portfolio. This enables consistent management of operational quality, risks, and investments, regardless of the specific technology involved.
Our Services
We support you every step of the way—from the operational readiness assessment through the transition to ongoing operations and controlled further development—whether for individual solutions or your entire AI portfolio. We work within your existing platform and tooling environment.
-
Assess & Transition
- Assess Readiness for Production – Verify that applications, architecture, processes, and responsibilities are suitable for production
- Identifying Risks and Requirements – Classifying system class, criticality, and operational, safety-related, and regulatory requirements
- Establish an operational model – Clearly define the scope of services, roles, SLAs, runbooks, escalation procedures, and approval processes
- Carry Out the Transition – Transition Knowledge, Access, and Responsibilities to Regular Operations in a Controlled Manner
-
Run & Monitor
- Ensure Normal Operations – Manage availability, incidents, service levels, and releases through established managed services processes
- Monitoring Performance and Quality – Continuously monitor technical stability, data and model drift, result quality, latency, and usage
- Make costs transparent – Track infrastructure, model, and token costs for each application and identify discrepancies early on
-
Optimize & Scale
- Optimize Models and Configurations – Targeted improvements to model routing, prompt and retrieval configurations, and resource consumption
- Implement changes in a controlled manner – Carry out retraining, model changes, and releases based on defined tests and approvals
- Scaling Operations – Extending standardized processes, automation, and common metrics to additional applications and departments
-
Govern & Assure
- Enforce Controls – Embed roles, permissions, guardrails, approval logic, and escalation rules into day-to-day operations
- Ensuring Traceability – Documenting relevant actions, decisions, changes, and human interventions in a manner that allows for auditing
- Provide verifiable evidence – Organize operational and control data in a structured manner for risk, audit, data protection, and compliance functions
- Support ongoing monitoring – Systematically record, assess, and track risks, incidents, and performance deviations
A Practical Introduction
Operational Readiness Assessment: Determine What Your AI Needs for Routine Operations
In a structured assessment, we evaluate your current or planned AI applications in terms of architecture, operational processes, quality, security, costs, and governance. You’ll receive a transparent assessment of operational readiness, prioritized areas for action, and a suitable roadmap—ranging from the operation of a single application to enterprise-wide management of your AI portfolio.
Why Arvato Systems?
We combine process expertise, AI, and automation to create productive solutions. From identifying the right use case to technical integration and secure operation: We choose the appropriate automation approach rather than hastily equipping processes with maximum autonomy. This ensures that we do not end up with isolated pilot solutions, but rather with transparent, scalable agent-based automation solutions that can be integrated into existing processes and systems.
One Partner. From Data to AI.
Your Contact for Data and Automation
