> ## Documentation Index > Fetch the complete documentation index at: https://docs.befailproof.ai/llms.txt > Use this file to discover all available pages before exploring further. # Core concepts > The small set of concepts used throughout Failproof AI. | Concept | What it means | What you can achieve | | ---------- | ------------------------------------------------- | ----------------------------------------------------------------------- | | Session | One agent task or run | Reconstruct an individual outcome | | Event | One recorded action in a session | Inspect a model call, tool use, error, human action, or policy decision | | Trace | The ordered and nested session view | Understand causality instead of reading disconnected logs | | Evaluation | A score or judgment on a session | Track quality, compliance, cost, or latency continuously | | Audit | A review of a selected session population | Search for failure patterns with a stated goal and cadence | | Finding | An evidence-backed failure discovered by an audit | See the failure mode, severity, and affected sessions | | Issue | A durable response record | Assign, discuss, and resolve a finding or alert incident | | Alert | A rule that detects recurrence | Notify responders when a known condition returns | | Policy | A rule evaluated during agent activity | Observe, block, or redirect risky behavior | | Deployment | A versioned policy rollout to machines | Control where a policy runs and roll it back safely | ## The reliability loop Start from evidence. A session shows what happened. An audit determines whether it is an isolated mistake or a pattern. A finding identifies the failure mode; an issue owns the response. A policy prevents the same behavior, while alerts tell you if the condition returns. A local `failproofai audit` scans local agent history. A recurring Cloud audit reviews sessions stored in Failproof AI Cloud. They are separate workflows with different scope and scheduling.