How does Glen fit a stateless pipeline?

Glen is the durable state your pipeline lacks. Call it as an MCP tool to read context at the start of a run and write observations at the end, so state persists across executions without a custom database.

Can multiple automations share the same memory safely?

Yes. Glen is org-scoped and tracks provenance per caller, so many automations read and write one store while you retain an audit trail of which automation wrote what.

Shared memory for automation engineers

As an automation engineer you build the systems that run the business unattended, integrations, scheduled jobs, event-driven pipelines, and increasingly AI agents that make decisions inside those flows. The problem is that automation is fundamentally stateless: each run fires and forgets. Your AI steps have no memory of prior executions, no shared context about an entity moving through the pipeline, no durable record of the decisions already made. You end up bolting a database onto every workflow that needs to remember, and each automation reinvents that state layer. Glen, shared memory for AI agents, gives your automations one durable, org-shared memory through a single MCP tool, so your flows stop running blind.

Connect Glen as an MCP tool and any automation with an AI step can retrieve relevant context and record what it learned in a single call. A pipeline processing an event pulls what the organization already knows about the entity before deciding how to handle it; a downstream job reads a fact an upstream job wrote minutes or days ago; an agent at the end of a flow records the outcome so the next run starts informed. You stop wiring a bespoke state store into every workflow and get one consistent memory layer instead.

Because Glen is org-scoped, the memory is shared across every automation you run and across the human-driven tools your team uses. One pipeline learns a durable fact; a completely different pipeline reads it next time, no shared mutable database you have to operate, no copy-paste between flows. And because Glen is a standard MCP server, the same memory your unattended automations write is readable from Claude, Cursor, or any other MCP client, so the work that runs while everyone's asleep and the work people do by hand compound into one store. You connect it once over OAuth or an API key and treat memory as shared infrastructure rather than per-workflow plumbing. For an automation engineer, Glen is the durable, queryable state your stateless flows have always been missing.

FAQ

How does Glen fit a stateless pipeline?: Glen is the durable state your pipeline lacks. Call it as an MCP tool to read context at the start of a run and write observations at the end, so state persists across executions without a custom database.
Can multiple automations share the same memory safely?: Yes. Glen is org-scoped and tracks provenance per caller, so many automations read and write one store while you retain an audit trail of which automation wrote what.