Agentic RM Productivity
Two streams of value, four rungs, grounded in Agentforce
Open with one question: where do your relationship managers lose more value today — preparing for conversations, deciding whom to contact, or following through afterward? The answer routes the rest of the call.
Start with one workflow, not the catalogue. Do not open by presenting every agent or every rung.
The ask
Agentic RM productivity delivered on Salesforce, with two streams of value that grow together. Agents make the RM faster. Signals make them right. Each rung adds new agent capability and a new class of signal, so the value compounds rather than repeats.
Most vendors sell one of these. The value is in running them together: an agent with no signal is a faster way to do the wrong work, and a signal with no agent is another alert nobody works.
Each rung is a complete offering that stands on its own, and each one unlocks agent capability the rung below cannot support.
The same four rungs for each line of business. Productivity on the left, signals on the right. Click any rung heading to collapse it.
One principle sets the commercial model, and it comes straight from the architecture.
Indicative ranges for planning. Scope drivers at S2: source systems in scope, identity resolution depth, upstream platform or Salesforce-native, number of signals, cohort size.
This play is one of three that sit inside the same framework. Sold on its own it works. Sold with the others it explains itself.
All figures are Moody's own published claims, not our measurements.
Every use case below carries a blue edge in the delivery view. Strongest fits first.
Connects to explains adjacent Zennify solutions. This equips a Salesforce account team to bring us into an account.
Bring us in when an agent pilot proved capability but never became a repeatable RM workflow.
Financial services workflow expertise, approved reusable components, signal-to-workflow integration, and a scoped delivery path with acceptance criteria.
Account context, sponsor access, current platform footprint, and entitlement validation. Tailor the conversation to the actual account team rather than assuming how any seller is compensated.
“Do you have a banking account where an Agentforce pilot showed capability but has not turned into a repeatable RM workflow? We can help qualify the business problem, validate the signal and data dependencies, and shape a bounded next engagement.”
One business-led discovery session, with the LOB sponsor present, producing an agreed use case and an agreed success measure. Not a capability demo.
An approved client one-pager and short demo, a persona-indexed discovery questionnaire, an offer card with inclusions and exclusions, a partner one-pager, and a target-account tracker. The launch test is whether a seller can identify the buyer, explain why Zennify, qualify the problem and secure the next meeting without an SME present. A finished HTML page is not a finished play.
Click any row for the full breakdown — what the RM does differently, reactive and proactive behaviour, where Data 360 comes in, and the library status.
| Capability | Lens | Rung | Type | AIforce surface | Value / outcome | Source |
|---|
Internal only. Platform feasibility, what needs an architecture decision, and what is coming.
An agent cannot rank a 200-relationship book in a single turn; retriever results and context windows both cap out. Precompute the ranking in Data 360 and have the agent reason over the top slice and act. Still genuinely agentic, and it is how every triage use case is built. Retest this. Salesforce stated at Dreamforce on 15 September 2026 that agents can read across hundreds of records at once. No limits have been published, so treat it as a claim to verify rather than a change to make — the precompute pattern works either way.
Agents can be invoked headlessly from a scheduled flow, which is how the population monitors work. Employee-facing agents execute as the invoking user, so a batch run needs a defined service-user context and sharing model. Confirm with the platform architect before build.
Agent behaviour is static until someone edits the instructions. A closed loop is real but the learning happens upstream: capture outcomes, retrain, reactivate, and the agent consumes the new version. Sell it as a data science capability, not an agent feature. It needs about two quarters of cohort data before it works.
Audit retention is roughly 90 days natively. Banking examination cycles are longer, so log export to Data 360 or an external store is an architecture requirement, not an option. This is also the clearest hand-off into the governance play.
Governance boundary. Platform governance holds as far as Data 360 has ingested or federated. Once a signal lives upstream in Databricks or Snowflake, that governance stops. We own that layer, which is an advantage, but only if we raise it before the client's risk team does.