Sales agent
Enriches and qualifies inbound leads against your real criteria, assembles cited account research, and drafts follow-ups for a human to send — with your CRM left cleaner than it was.
What does an sales agent do?
A sales agent handles the research and administrative layer of selling: it enriches and qualifies inbound leads against your written criteria, assembles account research from public sources and your own CRM history, drafts contextual follow-ups for a rep to review, and keeps CRM records current. Outbound sending stays behind human approval by default, because an autonomous agent emailing prospects is a brand risk that no efficiency gain justifies.
Today
Today a rep receives an inbound lead, opens LinkedIn and the company website to work out who they are, checks the CRM for prior contact, forms a view on whether the lead is worth a call, updates a few CRM fields inconsistently, and writes a follow-up from a template with whatever context they gathered. Most of that elapsed time is research and data entry rather than selling.
Why it hurts
Qualification requires research, research takes time, and leads that arrive outside working hours wait. Response speed is one of the strongest predictors of conversion, and it is the thing most easily lost to manual process.
Conversion rates fall for reasons that have nothing to do with the offer.
Written criteria exist in a document nobody consults. In practice each rep applies their own judgement, so pipeline quality varies and forecasting is built on inconsistent inputs.
Forecasts that cannot be trusted and wasted time on poor-fit leads.
Reps enter the minimum required to advance a deal. Fields go stale, research lives in someone's notes app, and every report built on the CRM inherits the gaps.
Reporting and territory decisions made on data nobody believes.
Runtime behaviour
Every trigger, the action it takes, and whether a human stays in the loop. This is the table to send to whoever has to sign off on the agent's decisions.
| # | Trigger | Action | Oversight |
|---|---|---|---|
| 1 | An inbound lead arrives from a form, chat or email | Enrich the record from public sources and check the CRM for prior contact or open opportunities | Autonomous |
| 2 | Enrichment completes | Score against written qualification criteria and attach the reasoning with sources | Autonomous |
| 3 | Lead scores above threshold | Assemble an account research brief and notify the owning rep in Slack or email | Autonomous |
| 4 | Lead scores below threshold or the signal is ambiguous | Route to a review queue with the score and its reasoning rather than silently discarding it | Human approves |
| 5 | A rep requests a follow-up | Draft a message grounded in the account research and prior conversation history | Human approves |
| 6 | Draft is approved by the rep | Send from the rep's account and log the activity against the CRM record | Human approves |
| 7 | Any research or qualification completes | Write structured fields back to the CRM, flagging conflicts rather than overwriting human entries | Autonomous |
How it is built
Scores every inbound lead against your written criteria and shows its work, so a rep can disagree with the reasoning rather than simply ignoring the number.
Briefs assembled from public filings, news, job postings and your own CRM history, with every claim linked to where it came from.
Contextual drafts prepared for human review, sent from the rep's own account once approved.
Structured writes with validation, conflict flagging instead of silent overwrites, and a source recorded for every field the agent maintains.
Results
When teams describe wanting an AI sales agent, they usually mean outbound prospecting at scale. The measurable win in the deployments we have run is almost always somewhere else: how fast a qualified response reaches an inbound lead.
Inbound leads decay quickly, and the delay is structural — qualification needs research, research takes a person, and people are not available at 11pm. An agent that enriches, qualifies and briefs within a minute of the form submission changes the conversion maths without sending a single autonomous message.
We keep external sending human-approved by default, and it is a deliberate asymmetry argument rather than caution for its own sake.
The upside of autonomous sending is a rep saving two minutes. The downside is a poorly judged message reaching a named prospect under your brand, at a volume that makes it hard to notice quickly. Those are not comparable magnitudes. So the agent drafts, and a human sends.
Internally the calculus flips: CRM updates, Slack notifications and research briefs are cheap to get wrong and easy to correct, so the agent acts on its own there.
A qualification score with no explanation is treated by reps as noise, and rightly so. A score with the criteria it matched, the signals it found and the sources behind them is something a rep can argue with — and an argument is useful, because overrides are exactly the labelled data that makes the criteria sharper next quarter.
Next Step
A 30-minute technical call with an engineer who has shipped this before — not a sales qualification round. You leave with a feasibility read, a rough shape for the build, and an honest answer about whether it is worth doing at all.