Use cases
Pipeline forecasting and hygiene
Spot stalled deals, missing fields, and at-risk forecast slippage before pipeline review.
The Monday pipeline review depends on data that’s only as fresh as the rep updated it on Friday — which is to say, not very. A pipeline Agent reads the whole pipeline state, flags what’s stalled, what’s missing, what’s likely to slip, and surfaces the gaps so the review actually starts with reality.
When this pays off
Stale pipeline
Half the deals in pipeline haven’t been touched in 30 days; reps update only when forced.
Forecast accuracy poor
Commit vs. close-rate gap grows quarter over quarter — leadership stops trusting the forecast.
Pipeline review is data hunting
The first 20 minutes of every pipeline review is reps explaining missing data.
Slipping deals caught late
Deals slip the week before the quarter ends — when it’s too late to do anything.
The shape of this use case
A pipeline Agent takes a pipeline snapshot and returns a structured analysis with flags.
Inputs
Pipeline state (deals, stages, dates, amounts), historical close-rate benchmarks, your forecast definitions.
Sources
CRM, prior quarter close data, deal activity logs, your sales methodology / MEDDICC framework.
Output
A pipeline brief — stalled deals, missing critical fields, slippage risk per deal, forecast vs. commit gap with reasoning.
Where to start
Two ways in, depending on whether you want something running today or built to your exact spec.
Clone a pre-built Agent
Open the Deal Health Analyst. More in the Marketplace.
Build your own
Start from scratch in the builder, or by describing it in Claude Code or Cursor with MCP & Plugins.
Either way, these are prompts your team can use on day one:
- “What deals look at-risk for Q3? Pull stalled deals, missing close dates, and weak MEDDICC scores.”
- “Compare this week’s commit vs. last quarter’s commit-vs-close at the same point in the cycle — are we tracking?”
- “Which 10 deals should the team focus on this week to hit number?”
Where to take it
Once it’s running, deepen it in three moves:
Let it improve
Feed back which at-risk signals predicted slippage into the Agent’s evals so flagging sharpens.
Common pitfalls
Noise overwhelms signal
If the Agent flags every deal as “at risk”, the team stops reading. Tighten criteria to what actually predicts slippage.
No outcome feedback
Without piping won/lost / slipped outcomes back to the Agent’s evals, the criteria can’t improve. Pipe deal outcomes to evaluations.
Missing the qualitative
Pure field-based analysis misses what AEs hear on calls. Have the Agent read recent activity notes and pick up sentiment signals.
Auto-changing CRM fields
Letting the Agent overwrite stage or amount fields without rep review breaks trust and reporting. Surface recommendations; the rep changes the field.