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AI GOVERNANCE
Observability and evaluation of AI agents in production
We set up the observability and evaluation loop for your AI agents: quality metrics, answer traceability and regression detection to operate with confidence.
Observability and evaluation of AI agents in production
AI agent observability matters when you need to measure and evaluate real agent behaviour in production, not only limit its answers. Before selecting tools, we define the journeys, data and decisions that must remain under your company's control.
We work from Barcelona and Madrid and remotely with teams across Spain, Europe, LATAM and the United States. The process combines decision sessions, verifiable releases and documentation so progress stays visible.
We hand over code, repositories, configurations and documentation in accounts owned by your company. You can therefore audit, evolve or change supplier without relying on private access or licences.
We set up the observability and evaluation loop for your AI agents: quality metrics, answer traceability and regression detection to operate with confidence. That is why we prioritise a maintainable, measurable solution ready to evolve without rebuilding its foundations.
THE CHALLENGE
The challenge of AI agent observability
without metrics, an agent degrades silently
Unaudited model outputs
AI agents generate answers nobody verifies, and errors only surface when the client complains.
Invisible per-call cost
Without consumption metrics it is impossible to know which prompts burn tokens and drive up operations.
Silent quality drift
A model or prompt change degrades responses with no system in place to detect it.
APPROACH
How we fix operating AI agents with quality data
We move forward with measurable decisions and releases that already add value.
Instrumenting every execution
We log inputs, outputs, latency and tokens of each call so any interaction can be reconstructed.
Evaluation with reference cases
We build an expert-annotated test set to measure accuracy and coherence in a repeatable way.
Metrics and alert rules
We define quality and cost thresholds that fire alerts when a prompt or model deviates from expectations.
Human review of anomalies
A responsible person reviews flagged cases and decides whether to fix the prompt, model or input data.
DELIVERABLES
What AI agent observability leaves ready
We deliver an operational foundation your team can govern and keep improving.
Agent traceability panel
Searchable log of every execution with inputs, outputs, latency and associated cost.
Labeled evaluation set
Reference cases with expected results to measure quality objectively and repeatably.
Drift and cost alerts
Alert system configured over quality, latency and consumption thresholds per agent.
Periodic performance report
Summary of metrics, incidents and corrective actions with traceability of each decision.
TRUST
Technology applied to real operations
We define evaluations and dashboards to catch regressions before they affect your customers.
- Scope, assumptions and risks visible before the project is committed.
- Integrations, permissions and data designed for real operations.
- Reviewable releases before the solution reaches the whole organisation.
- Code, configuration and documentation under your company's control.
FAQ
Questions about AI agent observability and evaluation
Have a project in mind?
Tell us what you need around AI agent observability and evaluation. We will help you turn it into a clear, viable delivery plan.
