Shared memory for VPs of Engineering
As a VP of Engineering you are accountable for the velocity and reliability of dozens of engineers who increasingly work alongside AI agents in Cursor, Claude Code, and CI. Every one of those agents starts each task cold, re-learning your architecture, your conventions, and the same hard-won lessons your team already paid for. That repeated relearning is invisible drag on the org you are measured by. Glen, shared memory for AI agents, gives every agent across your engineering organization one durable, org-scoped memory exposed as a single MCP tool, so what one team's agent learns is instantly available to all of them.
The leverage of AI in engineering is not in any single agent, it is in whether the whole organization compounds what its agents learn. Without a shared memory layer, each engineer's agent is an island: it discovers that a service has a quirky deploy step, that a module is being deprecated, that a particular library version breaks the build, and then it forgets, and the next engineer's agent rediscovers the same thing the hard way. Glen turns that loss into an asset. Connected over MCP, every agent in your org reads the same store before it acts and writes back what it learns when it finishes, so institutional knowledge accumulates instead of evaporating.
For a VP, the value shows up as fewer repeated mistakes, faster onboarding, and consistency across teams that no amount of wiki-writing achieves, because agents actually read memory at the moment of work, which is more than can be said for most documentation. Glen is org-scoped, so a fact your platform team's agent learns about the deploy pipeline is available to a product engineer's agent in a different repo, and because it is a standard MCP server, the same memory is readable from whatever clients your teams already use, no migration, no per-team silo. You connect it once over OAuth or an API key and govern it centrally: one store per org, every member reads and writes, provenance recorded on every observation. The result is an engineering organization whose collective agent intelligence improves week over week instead of resetting every morning.
FAQ
- Do I have to standardize my teams on one agent or IDE to benefit?
- No. Glen is a standard MCP server, so it works across Cursor, Claude Code, and any other MCP client your teams use. The shared memory is the common layer underneath whatever tools individual engineers prefer.
- How do I keep this governed across many teams?
- Memory is org-scoped with one store per org by default, every member reads and writes it, and every observation records who wrote it as provenance. You connect once over OAuth or an API key and manage access centrally.