Can it keep metric definitions and business rules consistent?

Yes. Agents record durable observations, the agreed definition, the rule and its rationale, and read them back when drafting or analyzing, so the team stops redefining the same concepts differently.

Do engineering and product agents see what I capture?

Yes. Glen is org-scoped, so a definition or requirement a BA's agent records is readable by engineering and product agents in the same org, keeping everyone aligned on one understanding.

Shared memory for business analysts

Business analysts turn messy reality into shared understanding: the requirements behind a feature, the business rules that govern a process, the stakeholder who must sign off, the definition of a metric that everyone argues about, the reason a workflow has an exception nobody remembers adding. As BAs adopt AI agents to draft requirements, map processes, and analyze data, each agent works without that institutional context, so it misstates a business rule, redefines a metric, or re-elicits requirements the team already documented. Glen, shared memory for AI agents, gives your team's agents one durable, shared memory exposed as a single MCP tool, so the domain knowledge you capture is recalled rather than reconstructed.

Business analysis is the work of capturing and reconciling context: what the business actually needs, why a rule exists, how a metric is defined, which exception applies and why. That context is exactly what gets lost, it lives in old requirement docs, process maps, and the BA's head, and AI agents make the loss sharper because each one starts without it. An agent asked to draft a spec or analyze a process does not know the agreed definition of a key metric or the documented reason behind a business rule, so it produces analysis that quietly contradicts the team's shared understanding. Glen makes that domain context durable: connected over MCP, an agent reads the shared store before it drafts or analyzes, picking up requirements, rules, and definitions, and writes back new findings, so the team's domain knowledge compounds.

For a business analyst this means the shared understanding you build becomes memory the agents consult rather than a thing you re-elicit each cycle. The agreed metric definition, the business rule and its rationale, the stakeholder constraint, all become context an agent recalls when it drafts requirements or maps a process, so analysis stays consistent instead of drifting. Because Glen is org-scoped, that knowledge is shared across analysts, product, and engineering agents alike, so a definition you record is the same one an engineer's agent builds against and a PM's agent reasons with. As a standard MCP server, Glen complements the documentation and data tools your agents already use, holding the durable domain rationale none of them capture, and is readable from Claude Code, Cursor, or any MCP client. Connect once over OAuth or an API key and let your domain knowledge compound.

FAQ

Can it keep metric definitions and business rules consistent?: Yes. Agents record durable observations, the agreed definition, the rule and its rationale, and read them back when drafting or analyzing, so the team stops redefining the same concepts differently.
Do engineering and product agents see what I capture?: Yes. Glen is org-scoped, so a definition or requirement a BA's agent records is readable by engineering and product agents in the same org, keeping everyone aligned on one understanding.