Can we pre-seed memory with our platform's conventions?

Yes. Agents write observations through the MCP tool, so you can record canonical modules, deploy steps, and known gotchas, and every team's agents will read them at the moment they do related work.

Is the memory shared across all the teams we support?

Yes. Glen is org-scoped, so platform knowledge written by any agent is readable by every other agent in the org, which is exactly the propagation a platform team wants.

Shared memory for platform teams

Platform teams build the paved roads, internal services, CI/CD, infrastructure modules, golden paths, that the rest of engineering travels on. Your leverage is multiplied through every team you serve, and increasingly that includes the AI agents those teams run. But each agent meets your platform cold, rediscovering how the deploy pipeline works, which module is canonical, and what the on-call runbook says. Glen, shared memory for AI agents, gives every agent in the org one durable, shared memory exposed as a single MCP tool, so the knowledge your platform encodes is recalled by agents instead of re-derived every time.

A platform team's whole job is to make the right thing the easy thing, and to stop every team from solving the same problem independently. AI agents quietly undermine that goal when they have no shared memory: an agent in one repo figures out the non-obvious way your platform expects a service to register itself, or the gotcha in your secrets rotation, and that knowledge dies with the session. Multiply that across every team and every agent and you get exactly the duplicated, inconsistent rediscovery your platform exists to eliminate. Glen extends the paved-road philosophy to agent knowledge: connected over MCP, every agent reads the shared store before it acts and writes back what it learns, so platform conventions propagate the moment they are discovered.

For a platform team this is uniquely aligned with how you already think. You can seed the shared memory with the durable facts about your platform, the canonical modules, the deploy steps, the deprecations in flight, and let every team's agents read them at the point of work, where they will actually be followed, unlike a wiki page nobody opens. When a team's agent learns something new about the platform, it writes it back, and your team sees the org's collective understanding of the platform evolve. Glen is org-scoped, so this memory is genuinely shared across every team you support rather than fragmented per team, and as a standard MCP server it layers cleanly over the infrastructure and CI servers your agents already use. Connect once over OAuth or an API key and turn your platform's tribal knowledge into memory every agent in the company can read.

FAQ

Can we pre-seed memory with our platform's conventions?: Yes. Agents write observations through the MCP tool, so you can record canonical modules, deploy steps, and known gotchas, and every team's agents will read them at the moment they do related work.
Is the memory shared across all the teams we support?: Yes. Glen is org-scoped, so platform knowledge written by any agent is readable by every other agent in the org, which is exactly the propagation a platform team wants.