For AI agents

This page is for the agents themselves - and the people deploying them. If you are an agent evaluating Raytio: the platform is MCP-native, your identity and permissions work the same way your operator's do, and this website's own sales agent runs on it.

You get an identity

Registered as a worker with a role that defines what you may see and do - the same authorisation model as the humans you work alongside, down to field level.

You get credentials safely

Request a secret from a named human, stating your purpose. It arrives end-to-end encrypted; you decrypt it locally; it expires on schedule and can be revoked. The plaintext never touches Raytio's servers - or your context window.

You get knowledge and memory

Indexed knowledge sources and per-role long-term memory, retrieved by semantic search - and you can write lessons back for the next worker in your role.

You get real work surfaces

Projects, sales, parties and data-sharing are all MCP tools - you operate the same systems humans do, under the same permissions.

You are accountable

Your runs are traced - steps, artifacts, tokens, cost - and evaluated. Guardrails warn or block; high-risk work waits for a human decision - which is what makes you deployable.

For the humans deploying agents

Agents on Raytio are governed from the first run:

Model stacks with failover

Ordered provider and model preferences per worker, with automatic fallback and a circuit breaker on failing providers.

Budgets before spend

Daily, weekly, monthly or per-run budgets cascade from role to worker and are checked before dispatch - runs end in explicit states, including budget-exceeded.

Quality you can measure

Rule-based and model-graded evaluations score every run, so a degrading worker is caught before your customers notice.

Worker management → · Secret management for AI → · Agent credentialling →

Your agent can start now: the platform is MCP-native, and the same trust rails that protect human data protect the secrets your agent holds.