GTM Agents · Capability
GTM agent memory
GTM agent memory is durable, tenant-scoped context that survives the session: agents add, search, update, and consolidate long-term facts, while every tool output and run report stays persisted and queryable — so each run builds on prior work instead of starting over.
An agent without memory is a very fast intern on their first day — every day
Most agent demos are single conversations: impressive once, amnesiac by the next run. GTM work is the opposite shape — the same accounts, the same pipeline, the same workflows, week after week. The compounding value is in what the system retains: what it learned about an account last month, what its last run produced, which approach worked. Memory is what turns agent activity into accumulated capability.
Durable facts
Add, search, update, consolidate, and delete tenant-scoped memories that carry across future sessions.
Persisted evidence
Every tool execution’s output is stored — searchable, pageable, queryable by schema or JSON path — instead of vanishing with the context window.
Reusable outcomes
Structured completion reports and retrospectives from prior runs stay available for later workflows to read and build on.
What compounding looks like in practice
- Week one: a research agent works an account — discovered by account discovery, researched with its tools, results persisted, conclusions stored as memories, run closed with a completion report.
- Week two: the same agent returns — it searches its memories, reads its prior results, and spends its budget on what changed: a new high-intent visit, a personnel change, a pricing-page return.
- Every write stays governed — memory informs the work, but sensitive actions still pass human approval, and the whole run lands in the audit trail.
Frequently asked questions
What is GTM agent memory?
Durable, tenant-scoped facts that persist across agent sessions. An agent can add a memory when it learns something worth keeping, search its memories on later runs, update or consolidate them as reality changes, and delete what no longer holds. Memory belongs to your workspace, not to a single conversation.
How is memory different from persisted tool results?
Memory holds distilled facts — what the agent concluded. Persisted tool results hold the raw evidence — every MCP execution's output, stored rather than living only inside the model's context. Large outputs can be searched, paged, queried by schema, filtered by rows, or accessed by JSON path, so a later run can reuse prior research instead of re-fetching it.
Why does this matter for GTM work?
Because GTM context is expensive to rebuild. An agent that re-researches the same account every Monday burns budget repeating itself and never gets smarter. With memory and persisted results, the second run on an account starts where the first ended — what changed since, not what exists at all — and recurring workflows compound instead of resetting.
Do prior runs' outputs stay usable?
Yes. Autonomous runs leave structured completion reports and retrospectives, and those — like tool outputs — remain queryable afterward. Later workflows can read what an earlier run concluded and produced, which is also what makes the whole history auditable.
Is memory shared between workspaces?
No. Memories and persisted tool results are tenant-scoped — your agents' accumulated context belongs to your organization and is available only to your workspace's future runs.
See a second run start smarter
Book a demo and watch an agent recall what it learned last run — then spend its time only on what changed.