SaaStr Agent增长：统一队列与10分钟入站转化

We ran a live edition of The Agents this week to walk through our agent-led growth stack as it stands today. We talked about “ask your agent instead of your developer” on last week’s episode, and this session goes into how that works in practice.

At SaaStr, agent-led growth means two different things:

• Using AI agents to run your own go-to-market. For us that’s mostly 10K, our main GTM agent.
• Getting picked by other people’s agents. When someone asks ChatGPT, Claude, Gemini, or an app’s built-in agent “what are the best B2B events to go to,” we want SaaStr to be the answer, and we’ve done specific work to get there.

Below is what we’re doing on both. Our full playbook, with every case study we’ve published, is at saastr.ai/agents and gets updated constantly.

10K Started as a Notion Helper 16 Months Ago. Now He’s Wired Into 30+ Tools.

10K is custom-built on Replit. He’s connected to more than 30 core GTM tools, plus another 36 or so that didn’t fit on the slide.

That covers most of go-to-market:

• Inbound and outbound
• Renewals and contacts
• Marketing and newsletters
• Enrichment
• Quote-to-cash (QuickBooks, Bill, and the rest of billing)

With those connections, 10K automates most of our GTM, with approvals and guardrails in place.

Sixteen months ago, Amelia used him to help with some outbound and to stop logging into Salesforce and Notion in three places every Monday. We added apps over time, the way you’d expand a new hire’s systems access as they prove out.

You don’t need 30 tools to start. Some of ours overlap, which we cover below.

Our Latest is The Prospector: One Queue for Every Inbound, Outbound Reply, Renewal, and Support Ticket

The most valuable thing Amelia built this year is what she calls the Prospector: one surface where everything in go-to-market lands and gets actioned.

We needed it because, with 3 people and 21 agents, things were falling through the cracks. We had an agent on inbound, and people were still emailing us directly. Outbound was going out from four tools. Renewals lived somewhere else, and support tickets lived somewhere else again.

Now it’s all in one place. An agent filters the noise and makes sure we respond to everyone, including current customers.

To build your own version:

• Spin up Replit, Lovable, Vercel, Base44, or whatever you use.
• Connect one tool per function. If you run Salesforce for sales and HubSpot for marketing, connect those two. Wherever inbound lands (a form, a CRM, a shared inbox), connect that. Whatever sends your outbound, connect that. If renewals live in a spreadsheet, connect the spreadsheet.
• Phase it in. In Amelia’s first week, the Prospector only had inbound. A reasonable sequence is inbound in week one, outbound by week four, then renewals and support.

The agent gives each record type a goal and keeps working it until the goal is met:

• Inbound: a booked meeting, or a handoff to a human if the agent can’t answer the question.
• Outbound: a booked meeting or a ticket purchase.
• Support: case closed.

Every record gets followed up, by a human or the agent, until it’s Closed Won, Closed Lost, or resolved. The agent runs 24/7 and doesn’t lose track of a thread.

Centralizing also improves the agent. In week one it doesn’t know how many touches an inbound needs versus an outbound versus a support case. With everything flowing through one queue, it learns which questions repeat, which outbound patterns get replies, and how many touches each motion takes.

Inbound: Research, a Personalized Prospectus, and a Meeting Booked in 10 Minutes

Every company has inbound, so this is where we’d tell most teams to start.

When a lead comes in, the Prospector runs four chained steps.

Step 1: First-party data before anything else

The agent first checks what we already know. Have they attended an event? Are they a newsletter subscriber? Is there an open opportunity? Did they download the sponsorship prospectus?

Most outbound and enrichment agents run only on third-party signals. Starting with first-party data gives the agent and the rep account history a newly assigned rep wouldn’t have.

If you have almost nothing connected on day one, give the agent whatever Salesforce data or spreadsheets you have.

Step 2: Third-party signals

We have three enrichment sources connected:

• Exa for company data. Ask most agents what to use for third-party data and they’ll name Exa.
• Sumble for company hierarchy, plus email enrichment when we need a decision-maker’s address. The person who inbounds is often not the buyer.
• CoreSignal for the individual’s LinkedIn, mostly when someone leaves their title blank.

Step 3: A personalized prospectus page

The agent generates a custom version of our sponsorship prospectus for that company. It includes a written intro using the account context and recommended packages based on what they asked about or what fits them. For most B2B companies, the equivalent is a personalized pricing and features page.

These pages have heat mapping. When a known prospect views one, the agent tells us who looked, for how long, and on which section, so we know whether to reach out.

Step 4: A personalized email with our own meeting booker

The agent drafts the email using everything from steps 1 through 3 and includes our custom booker, which 10K built to replace Calendly. The booker is tokenized to the company, and the agent suggests who the prospect should meet: Amelia, David, or both.

We can send from the Prospector dashboard or copy and paste the draft. Copy and paste gets used a lot, because when there’s already a thread going with someone, a reply inside that thread is usually better than a fresh email from the agent.

If the prospect books, the agent sends us a brief with the account research 10 minutes before the call.

The Base44 example

A real lead from Base44 came in (we changed the name in the demo). The agent surfaced:

• First-party: their CEO and leadership team had already attended SaaStr. The person who inbounded didn’t know that.
• They weren’t a newsletter subscriber.
• Exa: a vibe coding platform competing with Replit and Lovable, owned by Wix. Amelia had forgotten about the Wix acquisition, and it changed how she ran the first call. She asked directly how Wix factors into the decision.
• Sumble: CEO and founders listed, no CMO, no CRO, an SVP of Sales, and roughly 88 people in go-to-market.

They went through the flow and booked a meeting within 10 minutes. Not every inbound books that fast, but the ones with a real reason for reaching out convert better with this context in front of them. The research also stays attached to the account for the rest of the deal.

"Our AI VP Revenue 10K now books meetings with prospects in 10 minutes or less.

You can put much, much more care and info into those first prospect interactions than any human could.

Because it has access to so much first-party data.

Things even the prospect doesn't know… https://t.co/AspmcoN3lm pic.twitter.com/f7q0Eniwly

— Jason ✨👾SaaStr.Ai✨ Lemkin (@jasonlk) October 23, 2026

Outbound: We Run Four Outbound Tools and Only Unify the Replies

We have outbound agents running autonomously on Agentforce, Monaco, Artisan, and our own stack in 10K, plus a few other tools that also send email.

The Prospector does not pull in every outbound email those agents send. Amelia considered that and decided it was too much data to be useful. She focused it on follow-up instead.

The flow:

• Each tool sends from its native platform, and most write back to Salesforce.
• When someone replies, the Prospector pulls the reply in through that tool’s API or MCP.
• Only then does it create a Salesforce record. We send a lot of outbound and don’t want thousands of new records a day for people who never responded.
• A timer starts the moment the reply lands.
• The agent drafts the follow-up based on the original campaign.