Documentation Index
Fetch the complete documentation index at: https://docs.velatir.com/llms.txt
Use this file to discover all available pages before exploring further.
Overview
Every AI interaction captured by Velatir follows a defined lifecycle. Understanding this lifecycle helps you interpret trace statuses and configure your agents effectively.Lifecycle Stages
Received
A data source sends an AI interaction to Velatir. The trace is stored and marked as Received.At this stage, the trace data is captured and the platform assigns it to a session if related traces already exist. Processing begins immediately.
Agent Assessment
All active agents evaluate the trace in parallel. Each agent specialises in a different compliance domain and independently produces an intent.
Each agent returns one of three intents: Allow, Block, or Escalate. The trace moves to Assessed status once all agents have completed their evaluation.
| Agent | Domain |
|---|---|
| Data Protection | PII, financial data, credentials, health information |
| Gatekeeper | Access control, usage rules |
Action Resolution
The platform resolves the combined agent intents into a single action based on each agent’s role.
The most restrictive applicable action wins. If any Enforcer blocks, the trace is rejected. If any Enforcer escalates, escalation notifications are sent.
| Role | What the Agent Can Do |
|---|---|
| Observer | Reports findings only. Its intent is logged but does not affect the trace outcome. |
| Enforcer | Can block traces or escalate them. A Block intent rejects the trace. An Escalate intent surfaces the trace through connected channels. |
Trace Directions
Every trace has a direction that describes the type of AI interaction it represents.| Direction | Description | Example |
|---|---|---|
| Inlet | An incoming request to an AI system | A user submits a prompt to a chatbot |
| Response | An outgoing response from an AI system | The chatbot’s reply |
| Signal | A passive event | A tool call, system log, or background process |
Sessions
Related traces are automatically grouped into sessions. A session represents a complete conversation or workflow. For example, a user’s full chat exchange with an AI assistant. Sessions give reviewers the full context when evaluating an escalated trace. Instead of seeing a single interaction on its own, they can view the entire conversation.Monitoring Traces
Track trace progress in the web dashboard.- Traces view shows the status and agent assessments for all traces in a workspace
- Trace detail lets you inspect a specific trace to see each agent’s evaluation, the resolution logic, and the final outcome
- Sessions view shows all traces within a session for complete conversation context
Sessions
Understand how traces are grouped into sessions.