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Not every lead deserves the same effort. A scoring Agent evaluates incoming leads against your ICP and buying signals so reps focus on the accounts most likely to close — and disqualify the ones that won’t.

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When this pays off

High inbound volume

Leads are arriving faster than reps can manually triage them.

Equal time on tire-kickers

Reps are spending the same hour on a Fortune 500 prospect as a free-trial signup with no fit.

MQLs losing momentum

Marketing-qualified leads are sitting unrouted while marketing complains the funnel is broken.

ICP exists on paper only

You have an ICP doc but the team isn’t applying it consistently across new leads.

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The shape of this use case

A scoring Agent takes a lead record and returns a qualification assessment.

Inputs

CRM lead record, form-fill data, intent signals, web activity.

Sources

CRM, marketing automation, ICP and qualification framework in Knowledge, web search for missing context.

Output

A score with a reasoning summary — not just a number — plus a recommended action.

Delivery

Written back to the CRM as a field, posted to Slack for hot leads, used to auto-route to the right rep.

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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 Lia, the LinkedIn Lead Researcher & Qualifier. More in the Marketplace.

Build your own

Start from scratch in the builder, or by describing it in Claude Code or Cursor with Programmatic GTM.
Either way, these are prompts your reps can use on day one:
  • “Score this lead — they came in from the pricing page yesterday. Is it worth a first call?”
  • “Why did we lose Globex last quarter, and is this new account similar?”
  • “Walk me through the top 10 new leads from this week and what to do with each.”

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Where to take it

Once it’s running, deepen it in three moves:

Give it a playbook

Shape it with a prompt, your ICP in Knowledge, and Bulk Schedule to backfill.

Automate it on signals

Wrap it in a workflow that fires on a trigger.

Let it improve

Feed closed-won and closed-lost outcomes back into the Agent’s evals so “qualified” tracks what closes.

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Common pitfalls

Thin CRM data produces unreliable scores. Enrich the record before you ask the Agent to judge it.
Reps stop using their own judgment if the number is treated as final. The score is an input — surface the reasoning so reps can override it.
If the ICP in Knowledge hasn’t been touched in 18 months, the Agent is judging against last year’s reality. Review it quarterly against actual close-won data.
Without won/lost deals flowing back into the scoring criteria, the model can’t learn. Pipe deal outcomes back to the Agent’s evals.