Evaluation & Observability
AI evaluation and observability show how your AI systems actually behave: LLM testing and evaluation before and after release, AI agent monitoring on every run, and usage and spend tied back to teams.
What is this layer about
An AI system that nobody measures cannot be trusted with real work. Outputs vary from run to run, models change underneath you, and prompts drift. Evaluation answers whether the system is good enough, and observability answers what it is doing right now.
Evaluation runs before release and continues in production. Test suites catch regressions when a prompt, tool or model changes. Automated judges score outputs at scale, and human reviewers check the cases that matter most, so quality is a measured property rather than an opinion.
Observability follows each request across models, tools and agents. Traces, logs and metrics let teams find the failing step, see where time and tokens go, and attribute cost to the owner. Alerts and drift checks turn those signals into action before users notice.
What do we deliver
Offline evaluation and regression suites
Curated test sets run against every change to a prompt, tool, retrieval source or model, so a regression is caught in review and not discovered by a customer.
Online evaluation in production
Sampled live traffic is scored continuously against the same quality criteria, which shows whether real-world behaviour still matches what was approved before release.
LLM-as-judge with human review
Model-based judges score outputs at scale against written rubrics. Human reviewers sample and adjudicate the uncertain or high-risk cases, and their verdicts calibrate the judges.
Distributed tracing of agent runs
Each run is captured as a trace across model calls, tool calls and sub-agents, so teams can replay a decision and see exactly which step failed or slowed down.
Logs, metrics and dashboards
Structured logs and service metrics for latency, errors, throughput and quality scores feed shared dashboards, giving engineering, risk and business owners one consistent view.
Token and cost attribution, drift and alerting
Token use and spend are broken down by team, application and model. Drift detection and alert rules flag quality, cost or behaviour changes early enough to act.
Technology stack
- Evaluation frameworks
- Ragas
- DeepEval
- promptfoo
- OpenAI Evals
- Inspect
- lm-evaluation-harness
- LLM tracing and observability
- Langfuse
- Arize Phoenix
- LangSmith
- OpenLLMetry
- MLflow Tracing
- Telemetry standards and collectors
- OpenTelemetry
- OpenTelemetry Collector
- Jaeger
- Grafana Tempo
- Logs and metrics
- Prometheus
- Grafana
- Loki
- OpenSearch
- Elasticsearch
- Cost, drift and alerting
- OpenCost
- Evidently
- Alertmanager
- PagerDuty
- Grafana Alerting
These are the kinds of technologies this layer is typically built from; the choice of tools stays with the customer.
Business outcome
Quality you can show, not assume. Written criteria and repeatable tests give leaders evidence that an AI system meets the bar, and a clear record when it does not.
Faster diagnosis when something breaks. Full traces and logs let teams locate the failing step in a run quickly, instead of guessing which prompt, tool or model caused the problem.
Spend that maps to owners. Token and cost attribution by team and use case supports budgeting, chargeback and informed decisions about which model fits which job.
Safer changes and earlier warnings. Regression suites gate releases and drift alerts surface slow decay, so models and prompts can be updated with confidence.
Frequently asked questions
What does the Evaluation and Observability layer cover?
It covers how you check that AI output is good enough and how you see what the system is doing. That means quality checks on responses, traces of each run, and records that support review and audit.
How is output quality measured?
Your team defines the criteria, such as accuracy against reference answers, policy adherence or reviewer scores. The layer applies those checks the same way to every model, so you can compare models fairly and notice drift.
Can we trace what the AI did for a given request?
Yes, that is the aim. Each run is recorded with its inputs, the model used, the tools called and the outcome, so a reviewer or auditor can reconstruct how an answer was produced.
How does it relate to the safety and runtime layers?
Safety and Policy Controls decide what is allowed. The runtime and tools do the work. This layer watches both, measures results, and feeds findings back so policies and prompts can be improved.
Questions about your AI rollout?
Talk to us about how the seven layers fit your models and data.