Agent Runtime
The execution engine that turns an AI agent from a chat-window demo into a long-running, event-driven, restartable process you can trust with real operations.
Not a chatbot
A production agent is a long-running event-driven process, not a turn-by-turn chat session. It is triggered by webhooks, schedules, queues, change streams, or approved messages; it persists state across model calls and tool invocations; it survives restarts; it can take minutes or hours to complete a single task while staying observable and resumable.
- Event triggers (webhooks, schedules, queues, change streams)
- Durable state across tool calls and model calls
- Permission-scoped tool invocation through MCP
- Approval gates for high-risk actions
Launch pattern
We start with constrained workflows — one event type, one narrow tool surface, a generous human-approval gate, and tracing on every step. The workflow earns more autonomy as the eval set grows and the trace history shows the same decision being made the same way at scale.
Common substrates
LangGraph, the OpenAI Agents SDK, Mastra, and Inngest are common substrates we extend. Temporal and durable-execution patterns are useful when a task spans hours or days. The runtime is the contract — versioning, governance, evals, traces — not the underlying scheduler.
What it works with
Reads through the AI Platform's model gateway and MCP Tool Registry for every model call and tool invocation. Pulls context from Data Foundations through governed retrieval. Emits traces consumed by Workflow Evals and Observability. Routes high-risk actions to Human Approval. Surfaces failures to Closed-Loop Knowledge for clustering.
When you need it
Signals: an AI workflow that takes longer than a single HTTP request; tool calls with real side effects (sending email, updating production records, paying invoices); the need to debug last Tuesday's bad output a week later; per-tenant cost attribution required for billing. A chat session needs none of this; a production workflow needs all of it.