Agents sometimes need a secret to do their work — for example, an access token for an external service or a key for a third-party API. Agent credentials are how a worker asks a person for that secret, receives it in encrypted form, uses it for a bounded period, and has it withdrawn afterwards.
Every time an AI worker is dispatched to perform a task, the system creates an agent run — a structured record of what happened, how long it took, what it cost, and what it produced. Agent runs give you full visibility into worker activity without digging through log files.
Running AI workers costs money — every token processed incurs a charge. WRM's cost management features give you control over spending through budgets, visibility through cost tracking, and resilience through provider failover.
A domain is an area of specialisation — for example, billing, platform, compliance, or onboarding. Domains let the same role mean different things in different parts of the organisation: a product owner for billing and a product owner for platform hold the same role but carry different context, different repositories, and different accumulated knowledge.
When an AI worker completes a task, how do you know whether the work was any good? Eval scorers provide structured quality assessment of agent work — each scorer examines a completed run and produces a numeric score with optional reasoning.
Before an AI worker touches your codebase — and again after it finishes — guardrails run automated safety checks to catch problems early. They act as gatekeepers: a guardrail can warn you about a potential issue or block a dispatch entirely.
Not every change should be merged without a human looking at it first. Human-in-the-loop (HITL) adds approval gates to the agent workflow — high-risk work items pause after the merge request is created and wait for a human to review and approve before proceeding.
A knowledge source is a body of existing material — documentation, source code, API specifications, architecture notes — that workers can search by meaning rather than by filename. Knowledge sources give a worker access to what your organisation has already written down, while keeping the worker's prompt to the passages that matter.
Memories are persistent knowledge entries that WRM workers accumulate and read back. They let a worker draw on past decisions, lessons, and context each time it picks up a task.
Complex work rarely fits into a single task for a single agent. Orchestration lets you decompose large initiatives into coordinated work items that are dispatched in the right order, run in parallel where possible, and roll up into a unified result when complete.
WRM organises AI model access through a three-tier stack: providers, models, and availabilities. This page explains how the tiers relate, how credentials are managed, and how worker definitions select which models to use.
WRM separates the template for a worker from the running instance. A worker definition is the template; a worker is the instance. This page explains how the two relate, what each contains, and how to create custom worker definitions.
The WRM module provides a unified way to define, configure, and manage workers across your organisation. A "worker" in Raytio is any entity that performs work — a human team member, an AI agent, or an automated service. WRM gives each worker a structured identity and tracks the resources it needs to operate.
Raytio's Worker Relationship Management (WRM) module describes every worker — human or AI — and everything required to spin one up: role, template, system prompt, model stack, and toolset. WRM brings structured worker identity and resource management into the platform with the same access control and multi-tenancy guarantees as the rest of Raytio.