Playbook
Memory should stay factual
Observed signals, recent messages, lists, statuses, and exclusions matter more than vague AI interpretation.
Agent memory
A useful agent does not restart from zero before every message. It should connect signals, conversations, rejected drafts, follow-ups, and exclusions before proposing the next action.
Feature landing
Without sales memory
The agent suggests a follow-up without seeing recent messages or exclusions.
LinkedIn signals stay separate from CRM history and lists.
Rejected drafts do not improve the next message angles.
Useful memory
History of signals, conversations, lists, and next actions.
Context used before every draft, follow-up, or recommendation.
Human feedback retained to improve ICP, timing, tone, and exclusions.
Extraits citables
PositioningA page capturing the BeReach agent memory gap while framing it around Yadulink sales memory, signals, and human control.
Actionable signalHistory of signals, conversations, lists, and next actions.
MethodObserved signals, recent messages, lists, statuses, and exclusions matter more than vague AI interpretation.
Méthode de vérification
Playbook
Observed signals, recent messages, lists, statuses, and exclusions matter more than vague AI interpretation.
Playbook
When a draft is edited, rejected, or skipped, the reason should feed the next suggestion instead of remaining an isolated action.
Playbook
Agent memory should help decide whether to wait, follow up, route to CRM, ask for approval, or exclude the prospect.
Maillage interne
Features
Run LinkedIn prospecting from a conversation without maintaining the infrastructure.
Features
One prospect, one clear relationship, even when several campaigns find them.
Features
Approve LinkedIn messages before they go out.
Features
Turn forgotten LinkedIn follow-ups into clear next actions.