What kind of growth knowledge does Glen store?

Durable observations your agents generate, what an experiment concluded, what's true about a cohort, which approach worked, surfaced back into context automatically on the next relevant task.

Can analysis and automation share the same memory?

Yes. Glen is org-scoped, so the agent that analyzes an experiment and the automation that acts on a segment read from and write to the same store.

Shared memory for growth engineers

As a growth engineer you live at the intersection of code and experiments, building instrumentation, running A/B tests, automating lifecycle messaging, and increasingly wiring AI agents into the loop to personalize and analyze at scale. But your AI tooling is stateless. The agent that analyzed last quarter's experiment doesn't remember its own conclusions, the automation personalizing onboarding forgets what it learned about a cohort, and the hard-won knowledge of what actually moved the needle never accumulates anywhere an agent can reach. Glen, shared memory for AI agents, gives your growth stack one durable, org-shared memory through a single MCP tool, so insights compound across experiments instead of evaporating after each run.

Connect Glen as an MCP tool and any agent in your growth workflows can retrieve relevant context and record new findings in one call. An analysis agent reviewing a test pulls what the organization already learned from prior experiments on the same surface before drawing conclusions; a personalization automation reads a durable fact about a segment before tailoring a message; an agent at the end of an experiment writes back what worked and what didn't. The next experiment starts with the accumulated playbook rather than a blank slate.

Because Glen is org-scoped, the memory is shared across your whole growth function and the rest of the company. The lesson one experiment teaches is available to the next automation, the next analyst's agent, and the next teammate, no re-deriving conclusions, no insights trapped in a doc nobody's agent reads. And because Glen is a standard MCP server, the same memory is reachable from your IDE, your analysis tools, and your production automations, so the loop of build, measure, learn writes into one shared store. You wire it in once with an API key or OAuth and let the experiment knowledge compound. For a growth engineer whose edge is iteration speed and institutional learning, Glen is the memory that keeps every experiment's lesson alive for the next one.

FAQ

What kind of growth knowledge does Glen store?: Durable observations your agents generate, what an experiment concluded, what's true about a cohort, which approach worked, surfaced back into context automatically on the next relevant task.
Can analysis and automation share the same memory?: Yes. Glen is org-scoped, so the agent that analyzes an experiment and the automation that acts on a segment read from and write to the same store.