Can it hold clinical workflow and terminology context so agents stay consistent?

Yes. Agents record durable observations, the validated mapping, the privacy constraint, the reason a care pathway is shaped a certain way, and read them back before acting, so they reason from settled clinical facts instead of guessing.

How should we handle PHI and compliance?

Glen stores memory as plaintext to feed the LLM relevance and context passes, so treat your database credentials and Better Auth secret as the security boundary and avoid recording PHI. Memory is org-scoped, and you can isolate context with per-product memory stores.

Shared memory for healthtech teams

Healthtech teams build on context where being wrong carries real weight: the clinical workflow a feature must respect, the way a code set or terminology maps, the integration quirk of an EHR, the regulatory and privacy constraints that gate what a product can do, the reason a care pathway was modeled exactly as it is. As healthtech teams adopt AI agents to build, support, and analyze, each agent starts blind to that domain context, so it proposes something that breaks a clinical assumption or re-derives a mapping the team already validated. Glen, shared memory for AI agents, gives your team's agents one durable, shared memory exposed as a single MCP tool, so hard-won clinical and compliance context is recalled rather than reconstructed.

Healthcare context is deep, specialized, and unforgiving: an agent that does not know how a terminology maps, which privacy rule gates a data flow, or why a clinical workflow has the steps it does will produce work that looks plausible and is quietly wrong, and in a clinical setting that gap matters. The knowledge that prevents it, the validated mapping, the regulatory constraint, the integration quirk, the rationale behind a care pathway, usually lives in a handful of domain experts and scattered docs. Glen makes it durable: connected over MCP, an agent reads the org's accumulated knowledge before it builds, supports, or analyzes, picking up the constraints and the reasoning behind prior decisions, and writes back what it learns, so the team's clinical and compliance knowledge compounds instead of bottlenecking on a few people.

For a healthtech team this means the agents reason from settled domain facts instead of guessing at them. The terminology mapping, the privacy constraint, the EHR integration quirk, the documented reason a workflow exists, all become context an agent recalls before it acts, so engineering, support, and analytics agents stay consistent with the same validated knowledge. Because Glen is org-scoped, that knowledge is shared across every agent in the company and stays with the org rather than an individual expert, which matters in a domain that prizes consistency and traceability. As a standard MCP server, Glen complements the systems your agents already touch, holding the durable institutional knowledge none of them capture, and is readable from Claude Code, Cursor, or any MCP client. Connect once over OAuth or an API key, keep separate stores per product if you need to, and let your domain context compound. Note that Glen stores memory as plaintext for the LLM pipeline, so treat your database credentials and secrets as the security boundary and keep PHI out of what agents record.

FAQ

Can it hold clinical workflow and terminology context so agents stay consistent?: Yes. Agents record durable observations, the validated mapping, the privacy constraint, the reason a care pathway is shaped a certain way, and read them back before acting, so they reason from settled clinical facts instead of guessing.
How should we handle PHI and compliance?: Glen stores memory as plaintext to feed the LLM relevance and context passes, so treat your database credentials and Better Auth secret as the security boundary and avoid recording PHI. Memory is org-scoped, and you can isolate context with per-product memory stores.