Evals, tracing, guardrails, and observability
Build the evidence, controls, and feedback loops required to run intelligent systems safely.
- 16 minutes
- advanced
- Reviewed 2026-07-16
What is it?
Evaluations measure outcome quality. Traces reconstruct model and tool steps. Guardrails constrain inputs, outputs, and actions. Observability connects quality with errors, latency, cost, and operational health.
Why does it matter?
A demo proves one path can work. Production evidence shows whether many real cases work reliably, safely, and economically after models, prompts, tools, and data change.
The mental model
A production system is a versioned experiment with tests, traces, limits, alerts, and an accountable owner.
A simple example
A release suite tests 100 representative tickets, blocks unsafe tool arguments, traces every call, alerts on rising failure rate, and compares cost and latency with the previous version.
What it is not
It is not one safety prompt, a success-rate vanity metric, or logging sensitive content without a clear purpose and retention policy.
Learn this first
These ideas make the lesson easier to place.
- Model selection
- Agent loop
- Orchestration
Your first 60 minutes
Use one focused hour to make the idea concrete.
- Define ten representative cases with expected outcomes.
- Record model, prompt, tool, and data versions.
- Trace one success and three failure types.
- Set limits for steps, latency, cost, and permissions.
Build this first
Create a production-readiness sheet with offline evals, trace fields, alert thresholds, rollback criteria, and an incident owner.
When not to use it
Do not collect every possible log. Minimize sensitive data and choose controls that match real risk; simple deterministic workflows may need ordinary tests and monitoring rather than AI-specific machinery.
What to learn next
Apply the complete production mindset in the supervised-agent lab.