AIOps and LLMOps: smarter IT operations, accountable production AI
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AIOps uses AI to connect operational events, reduce noise and support incident prevention. LLMOps brings language models and agents into production with evaluation, versioning, cost controls and monitoring. Loonar designs, implements and supports both disciplines, linking dependable IT operations with clear accountability for the AI your organization runs.
In short
- Connect signals before automating actions.
- Evaluate prompts and models before release.
- Tie AI spending to the work it supports.
How do you know you need aiops and llmops?
Alerts without context
Events arrive from disconnected systems and compete for attention. Without deduplication and service context, responders investigate symptoms separately, repeat checks and struggle to distinguish meaningful changes from the background noise of daily operations across their environment.
Pilots without an operating model
A demonstration works, but nobody owns the path to production. Acceptance criteria, operational integration and support responsibilities remain unclear. Each change requires improvised decisions while data exposure and unpredictable model behavior receive inconsistent treatment across teams.
Spending without attribution
Token usage, compute and storage appear on separate bills without links to applications or user journeys. Teams cannot explain changes in spending or decide which resources to adjust without risking the quality experienced by customers and employees.
Changes without evidence
Prompts and models change without a record of the evaluation data used. When an answer deteriorates, teams lack comparable versions and approval history. Finding the cause or restoring a known behavior becomes a slow, uncertain manual investigation.
How does it work?
Event correlation
We group signals by service, dependency and operational context. Deduplication retains relevant evidence and supports triage, while reviewable rules and human oversight keep consequential decisions accountable to the service owner.
Anomaly detection
We establish behavioral baselines and deviation signals to support early warnings. Available history helps test assumptions, without treating forecasts as certainty or replacing investigation with automated classifications that lack service context.
Versioned AI assets
We organize models, prompts and datasets with ownership, metadata and history. Releases reference evaluated assets so teams can compare changes, investigate regressions and restore approved versions when the situation requires recovery.
Evaluation and safeguards
We build task tests, quality criteria and input and output controls. Release checks require evidence proportional to risk and route ambiguous situations to human reviewers before broader production exposure.
Serving and observability
We instrument model serving and track latency, consumption, drift and quality. Failures connect to deployed versions, with containment, recovery and investigation procedures designed around the needs of critical applications.
Access and auditability
We apply role-based permissions, data protections and usage records. Defined retention and content access rules support security reviews while limiting unnecessary exposure of sensitive information in prompts and generated responses.
How does Loonar deliver?
Discovery
We map event sources, AI applications and data flows. Critical decisions, telemetry gaps and ownership inform a bounded starting scope that can be evaluated without promising unrestricted automation across operations.
Design
Loonar designs event correlation and the model operating lifecycle with service owners. Together we define quality criteria, autonomy boundaries and spending policies that reflect actual operational responsibilities and business risks.
Implementation and integrations
We connect telemetry, asset registries and release workflows. Evaluations, permissions and dashboards are tested in a controlled environment before extending the approach to applications already serving production users and workloads.
Training
We train operations, engineering and business owners to interpret signals and evaluation results. Investigation, approval and recovery exercises leave teams with documented procedures and explicit conditions for human intervention when risk increases.
Operations and continuous improvement
We support regular reviews of alert quality, spending and model behavior. Correlations and evaluations evolve with new evidence, while change history and shared decisions remain available to each application owner.
What results do we measure?
- Alert noise reduction
- Compares actionable and duplicate alerts across equivalent periods without sacrificing operational coverage.
- Incidents prevented
- Records preventive interventions with evidence of risk rather than assuming every absence of failure proves prevention.
- Time from model to production
- Tracks the interval between a candidate model version and its approved release.
- Cost per inference
- Attributes usage and infrastructure costs to each application and model version.
- AI quality incidents
- Tracks confirmed inadequate outputs against explicitly defined quality criteria.
Where do AIOps, MLOps and LLMOps fit?
These disciplines manage different operational concerns while sharing evidence and accountability.
| Aspect | AIOps | MLOps | LLMOps |
|---|---|---|---|
| Purpose | Support IT operations | Operate machine learning | Operate language applications |
| Managed assets | Events and services | Models and datasets | Prompts, models and agents |
| Measures | Noise and response | Drift and performance | Quality, latency and cost |
| Controls | Action validation | Model validation | Response and tool evaluation |
Where do we apply it?
Financial services
A financial institution needs operational event correlation without exposing sensitive records in prompts. We establish data minimization, access rules and task evaluations. Responders gain service context, while the AI team retains evidence of approved versions and knows which decisions require human review before any consequential action proceeds.
Retail and e-commerce
Customer assistants and recommendation services face shifting demand. We connect consumption, latency and output quality to the journeys they support, with application-level limits. Model changes pass evaluations before release, and deviations trigger investigation without assuming every demand spike indicates a technical failure or poor model behavior.
SaaS
Product teams embedding agents need visibility into responses and tool calls. We define task tests, version traceability and autonomy policies. Engineers can investigate regressions, compare configurations and manage consumption using production evidence rather than relying solely on occasional assessments carried out while building the application.
Frequently asked questions
What is the difference between AIOps and MLOps?
AIOps applies AI to IT operations, connecting events and helping responders investigate service issues. MLOps manages the lifecycle of machine learning models, from training through production operation. They overlap when models inform operational decisions. The practical distinction is ownership: service health needs accountable operators, while model behavior needs evaluation, version control and monitoring appropriate to its intended use.
What is LLMOps?
LLMOps is the operating discipline for applications built on language models. It includes prompt versioning, evaluations, data protection, LLM observability and consumption controls. For agents, it also covers permissions and tool execution. The goal is to make changes testable, detect regressions and support recovery. A fluent answer is not evidence of correctness, so evaluation must reflect the task and its consequences.
How can we control production LLM costs?
Start by attributing usage to applications, versions and tasks. Track tokens, infrastructure, retries and tool calls alongside delivered quality. Then assess context limits, suitable caching and model selection based on actual requirements. Decisions should account for latency and risk, not just token prices. Budget alerts need owners and response procedures so cost controls do not create unexpected service interruptions.
How do we evaluate an agent’s response quality?
Define representative tasks and success criteria before selecting evaluators. Combine automated tests, human review and verification of executed actions, including tool use and permission boundaries. Record the tested context and version. Adversarial cases and known regressions belong in the evaluation set. In production, track confirmed failures and route sensitive decisions to accountable people rather than trusting a single aggregate quality score.
How should we govern developers’ use of AI?
Specify which data may leave the environment, which models are approved and which actions need authorization. Loonar designs and implements these controls, trains teams and supports ongoing operational reviews. Catalogs, access records and evaluations make policies usable in everyday engineering. Governance should guide work, protect information and preserve useful evidence without collecting sensitive prompt content simply because logging is technically possible.
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