Can it remember past decisions and the reasoning behind them?

Yes. Agents record durable observations, the decision, the tradeoffs, the experiment result, and read them back when drafting specs or weighing requests, so the team stops relitigating settled questions.

Do engineering and design agents see the product context I record?

Yes. Glen is org-scoped, so a decision a PM's agent records is readable by engineering and design agents in the same org, keeping the whole team aligned on the why.

Shared memory for product managers

Product managers hold the why behind a product: the customer problems you keep hearing, the decisions you made and the tradeoffs behind them, the experiments you ran, the bets you deliberately did not take. As PMs adopt AI agents to synthesize feedback, draft specs, and analyze usage, each agent works without memory of that context, so it loses the thread of past decisions and re-surfaces problems the team already addressed. Glen, shared memory for AI agents, gives your team's agents one shared, durable memory exposed as a single MCP tool, so product context persists and is recalled rather than reconstructed every time.

Product management is the discipline of accumulating and applying context: what customers actually struggle with, why a feature was built the way it was, what an experiment taught you, which idea was shelved and why. That context is easy to lose, it lives in old docs, closed tickets, and the PM's head, and AI agents make the loss sharper because each one starts blind. An agent asked to summarize feedback or draft a spec does not know the same request was deprioritized last quarter for a documented reason, or that an experiment already answered the question being asked, so it re-treads decided ground. Glen makes product context durable: connected over MCP, an agent reads the shared store before it synthesizes or drafts, picking up past decisions and their rationale, and writes back new insight, so the team's product memory compounds.

For a PM this means the why behind the product becomes memory the agents consult rather than archaeology you redo. The recurring customer problem, the decision and its tradeoffs, the experiment result, all become context an agent recalls when drafting a spec or weighing a request, so the product evolves coherently instead of cycling through the same debates. Because Glen is org-scoped, this memory is shared across product, engineering, and design agents alike, so a decision you record is visible to the engineer's agent implementing it and the designer's agent shaping it. As a standard MCP server, Glen complements the project-tracking and analytics servers your agents already use, holding the durable product rationale none of them capture. Connect once over OAuth or an API key and let your product context compound across every agent on the team.

FAQ

Can it remember past decisions and the reasoning behind them?: Yes. Agents record durable observations, the decision, the tradeoffs, the experiment result, and read them back when drafting specs or weighing requests, so the team stops relitigating settled questions.
Do engineering and design agents see the product context I record?: Yes. Glen is org-scoped, so a decision a PM's agent records is readable by engineering and design agents in the same org, keeping the whole team aligned on the why.