LangChain's first sales agent researches new leads and drafts a message to each one. It posts the draft in Slack, where a rep can review it. The agent does the preparation, and the rep decides whether the message should go out.
Vishnu Suresh showed this process at the Deepline x Exa Claude Code + GTM Lightning Talks. His team started with a job reps already needed to do, researching people who had contacted the company. They then expanded the agent's work to help reps follow what was happening at customer accounts.
The other talks covered different jobs. AssemblyAI built an agent to help customers use its product. Exa connected web research to draft messages. Composio showed how agents get permission to use company tools, and Deepline used attendee research to prepare event follow-up.
LangChain sends outreach to Slack for approval
Before drafting a message, LangChain's agent checks what the team already knows about the lead. It reads the customer relationship management system, or CRM, alongside its research. The rep receives the draft in Slack and can review it there.
Vishnu's team later expanded this work to customer accounts. Reps can see how a customer uses the product and what the company is doing publicly, including hiring and website activity. The agent also brings together marketing interactions and information about risks, competitors, and possible expansion.
Waiting for a rep raises a software problem. The agent needs to remember what it has done while it waits. LangGraph saves that progress in checkpoints. An interrupt pauses the work for a response, after which the agent can continue from the saved point.
LangChain · Vishnu Suresh on LinkedIn · Vishnu Suresh on X
AssemblyAI gives its support agent the customer history
Matt Lawler showed Joey, the agent AssemblyAI uses to help people who sign up and start using the product themselves. Those customers need answers to technical questions, help when something breaks, and sometimes a conversation with sales.
Joey can read the product documentation and the customer's previous conversations. It can also check service status and use debugging tools. That gives it information about the problem beyond the customer's latest message.
AssemblyAI also lets developers turn recorded or live speech into text. Its realtime API, the interface a developer connects to, sends back text as someone speaks. It uses a WebSocket connection to keep that stream open.
AssemblyAI · Matt Lawler on LinkedIn
Exa searches for prospects and researches accounts
Scott Langille's demo followed a prospect from research to a draft message. Exa searched for potential customers, then gathered information about their companies. The agent saved those findings with each person's record and used them when it wrote the message.
The research and the message stayed connected. A rep could see the company information that the draft referred to.
Exa documents its search options in the Search API guide.
Exa · Scott Langille on LinkedIn · Scott Langille on X
Composio controls what an agent can access
Sujay Choubey showed an agent working across legal documents, Slack approvals, and customer records. Before an agent can do that work, someone has to connect the apps and decide what it may read or change.
Composio handles those connections for each user. Its authentication docs describe how an agent asks a user to connect an app. Its permission checks then compare each requested action with what that user has allowed. Those checks include OAuth scopes, which specify the access granted to a connected app, and lists of allowed actions.
Composio · Sujay Choubey on LinkedIn · Sujay Choubey on X
Deepline connects event research to follow-up
Watch Jai's Deepline live build
Jai Toor used the event itself as the subject of his live build. With Claude Code and Deepline, he built a place to collect what the team knew about attendees, research them, and score which people to follow up with.
The research then fed into follow-up tasks. The demo connected the work of learning about an attendee with deciding what to do after the event.
Deepline · Jai Toor on LinkedIn
Related discussions
Sales and marketing teams have discussed how they use Claude Code in r/gtmengineering, and how to coordinate agents and keep records of their work in r/ClaudeCode.
Zapier also published GTM agent skills and automations.
Sources and further reading
- LangGraph persistence and LangGraph interrupts for checkpoints, state, and human approval.
- AssemblyAI realtime speech-to-text and Voice Agent API for live conversation context.
- Exa Search API guide for agent search and structured context retrieval.
- Composio docs, authentication, and protection for tool use, OAuth, scopes, and allowlists.
Full recording
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FAQ
What is a GTM agent?
A GTM agent uses an AI model and connected tools to do sales or marketing work. Examples in these talks include researching leads and drafting outreach for a rep to approve.
Why do GTM agents need human approval loops?
An approval step pauses the agent before it takes an action the team wants to review. In the LangChain demo, reps review draft outreach in Slack.
How does Deepline fit into GTM agent workflows?
Deepline connects Claude Code to company and contact data, email verification, and CRM updates. Jai used it to build an event research and follow-up workflow.