How we get back to every inbound lead
A fresh lead goes stale fast. Someone who fills in your form on Tuesday is far more likely to talk if you reply that Tuesday than if you get round to it on Friday. Trouble is, a decent reply takes homework: who is this company, what do they do, why did they sign up? When the pipeline is busy, the homework is the first thing to go.
So every new lead gets an agent. It looks into the company, writes a follow-up that's actually about them, and suggests a time to talk. Here's how we run it on Machine: what it connects to, the steps it takes and the limits we set.
The problem
The follow-up that wins the meeting is the one that proves you read the form and get what the company does. That needs a few minutes of digging for every lead, and a few minutes for every single inbound adds up to more time than a growing team can spare. So replies go out bland, or they go out late. Either way, deals slip away.
Templates speed up the sending but not the personal touch, and the personal touch is what counts. Then setting up the call means another round of emails. The work that actually moves a lead along is the work that keeps getting dropped.
What we built
We connected our HubSpot pipeline to an agent on Machine. On a schedule, a check starts a fresh, isolated session, a cloud sandbox, with scoped access to only what a follow-up needs: the lead's record, the web for research, our email and the team calendar. For each new lead the agent does its homework, writes a personal message and suggests a call time. A person signs off before anything is sent.
How it works
Check HubSpot on a schedule
Every 15 minutes a scheduled trigger looks through HubSpot for new leads, and each check opens a fresh session, a cloud computer (sandbox) of its own. If one check finds several new leads, they all run in that one session, but each lead is treated as a separate job. If the research or the draft fails for one lead, the others carry on untouched. Nothing is shared from one check to the next; what carries forward is the marker the agent writes back on each HubSpot record.
Hand it our sales playbook
Every session loads how we follow up as skills and memory: how we position ourselves, the voice we write in, what a good qualifying question looks like and follow-ups that have worked before. The agent writes to that bar instead of making up its own, and its memory updates as deals progress.
Decide what it can reach
The agent works through scoped connectors, connections that Machine brokers on the server so the model never sees a raw token. With them it can:
- Look into the company: it reads the lead's website and public details to learn what they do and why they got in touch.
- Read and write HubSpot: the full lead history goes in, and its research notes go back onto the record.
- Write the email: a personal follow-up shaped by our playbook, ready to go.
- Suggest a time on Google Calendar: it checks when the team is really free and offers a specific slot for a call.
Put a person before the send
Research and drafting are fair game; sending on its own is not. Each follow-up stops at a human approval gate and sits as a draft until someone checks it and sends it. Credentials are stored encrypted in the Secrets Manager and only handed to the agents you grant them to.
Let every lead get worked
Once that's in place, new leads show up with the work already done. The company is researched, the record is updated, a personal email is drafted and a call time is suggested against real free slots. Whoever is on sales reads the draft, changes what they like and sends it, rather than starting every follow-up from a blank page.
The pattern
Link the CRM with a trigger that spots new leads, give the agent scoped connectors to HubSpot, email and the calendar, write the sales playbook down as skills and memory, and make a person approve every send.
Guardrails
Letting an agent contact leads is a question about your brand as much as your sales. Here's what Machine puts in place:
- Isolation. Every check runs in its own sealed sandbox: a fresh session that may cover several new leads, each treated as a separate job so one failure never holds up the others. The drafts it writes are the only thing that comes back out.
- Scoped secrets. HubSpot, email and Calendar credentials are stored encrypted in the Secrets Manager and only handed to the agents you grant them to.
- Human approval gate. Nothing reaches a lead until a person has read the draft and sent it.
- Everything is code. The agent's persona, skills and permissions are files in the repo. They're versioned and only change through a reviewed change request, not a setting in a dashboard.
The outcome
Each new lead now turns up with the homework finished, a personal draft written and a call time suggested, waiting on a person to approve it instead of sitting untouched. The team spends its time choosing which leads to chase, not repeating the same research again and again.
Four things hold it together: a sealed sandbox for each run with every lead handled on its own, a secrets manager that brokers the HubSpot, email and Calendar tokens, a person approving anything before it reaches a lead, and memory that gets sharper as deals move.
More use cases
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Once a day a Machine agent reads every commit merged since its last pass, finds the doc pages those changes made wrong, rewrites them and sends you one pull request to review.
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