Diagnosis and architecture
We audit your current pipeline: models in use, prompt flows, business integration points and observability gaps. We deliver a technical risk map and the LLMOps layer design tailored to your stack.
Gobernanza tecnica IA
LLMOps and AI Agent Evaluation
A model that works in a demo can fail in production for reasons invisible without proper instrumentation. LLMOps is the operations engineering discipline that keeps your AI systems reliable, auditable and continuously improving.
Deploying an AI agent or a RAG system is only the first step. The real challenge comes when the model handles thousands of real queries: hallucinations that nobody catches, silent quality drift, inference costs growing out of control, and no trace to audit what the system decided or why. LLMOps — the discipline that applies operations engineering principles to large language model systems — exists precisely to solve that problem.
At Summum IA we instrument your AI pipeline with end-to-end observability: every model call is recorded with its prompt, response, latency and cost. On top of that traceability we build automated evaluations that measure coherence, context fidelity, absence of bias and adherence to company policies. When a metric drifts beyond the agreed threshold, the team receives an alert before the issue ever reaches the end user.
The EU AI Act imposes documentation, human oversight and risk management obligations for general-purpose AI systems and high-risk AI. Governance and transparency provisions for GPAI models entered into force on 2 August 2025; full enforcement of the regulation arrives on 2 August 2026. A well-designed LLMOps layer is the technical foundation that makes it possible to meet those obligations without turning them into a manual burden: logs, evaluations and prompt-change records are exactly what auditors will ask for.
The LLMOps and AI Agent Evaluation process.
The process · four stagesInstrumentation and traceability
We implement observability on your LLM applications using OpenTelemetry and tools such as Langfuse, Arize Phoenix or LangSmith depending on the environment. Each trace captures prompt, response, tokens, latency and actual cost.
Continuous evaluation suite
We build test sets with your real data and define metrics: context fidelity, hallucination rate, RAG retrieval quality, policy compliance. Evaluations run in CI/CD and periodically in production.
Governance, alerting and continuous improvement
We establish quality thresholds, dashboards for technical and business teams, prompt versioning workflows and rollback procedures. Everything is documented to meet AI Act audit requirements.
What is included
What LLMOps and AI Agent Evaluation includes.
The operational detail: what we deliver as part of the work and what we keep alive afterwards.
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LLM risk map
Analysis of the points where the system can fail silently: hallucinations, data leakage, quality drift and inference cost overruns.
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Trace instrumentation
Integration of the observability layer into your existing stack. Compatible with LangChain, LlamaIndex, direct API calls and custom agents.
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Automated evaluation suite
Battery of evaluations with LLM-as-judge, Ragas metrics for RAG and regression tests that run with every model or prompt change.
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Operational dashboards
Quality, latency and cost dashboards for technical teams, with configurable alerts and executive visibility in a summary view.
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Prompt versioning and control
Historical record of every prompt with its measured impact on quality. Enables version comparison, regression detection and controlled rollback.
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AI Act documentation
Generation of the technical records required by the European regulation: decision logs, human oversight records and continuous evaluation evidence.
Summum cluster
How it connects with its sisters.
The technical governance of your AI models connects directly with Summum Calidad's ISO 42001 implementation and with the AI Act risk assessment led by Summum Consultoría.
calidad
Summum calidad
ISO 42001 is the AI management system that frames organisationally what LLMOps implements at the technical layer; both services complement each other for complete governance.
View service → consultoríaSummum consultoría
The legal-consultancy team at Summum Consultoría assesses the regulatory risk of your AI systems under the AI Act; LLMOps provides the technical evidence that analysis requires.
View service → iaSummum ia
When models run on your own or on-premise infrastructure, LLMOps applies equally to maintain quality and auditability without relying on cloud-provider metrics.
View service →Frequently asked questions about LLMOps and AI Agent Evaluation.
What is the difference between LLMOps and MLOps?
MLOps covers the lifecycle of classical machine learning models (training, versioning, deployment). LLMOps inherits those principles but adapts them to the specific characteristics of large language models: non-determinism, prompt dependency, extended context management, cost per token and multi-step reasoning traceability. In practice, most teams that already have MLOps in place need an additional dedicated layer for LLMs.
Is LLMOps necessary if we only use an internal copilot with few users?
It depends on the risk level of the decisions that copilot supports. If the system accesses confidential data, generates documents with legal effect or advises on critical processes, the absence of observability is an operational risk and, since August 2025, potentially a breach of the AI Act. For low-risk use cases, lightweight instrumentation with basic alerts is usually sufficient as a starting point.
How is the quality of LLM responses measured?
The most widely used metrics include context fidelity (the model answers with what the source document actually says), answer relevance, absence of factual hallucinations and adherence to internal policies. They are measured with automatic evaluators based on another model (LLM-as-judge), with frameworks such as Ragas for RAG systems, and with periodic human review over stratified samples.
What does the EU AI Act specifically require regarding observability?
The regulation requires, for high-risk systems and GPAI models, retaining records that allow verification of system behaviour, documenting changes made and demonstrating that effective human oversight exists. A properly designed LLMOps layer automatically generates that documentation: inference traces, evaluation records, prompt versioning and quality drift alerts.
How long does it take to implement a basic LLMOps layer?
For an LLM application already in production, the initial instrumentation — trace integration, first metrics and dashboard — can be completed in two to four weeks depending on pipeline complexity. The full evaluation suite and governance workflows are typically developed over a two-to-three-month project.