How to Build an AI Sales Agent End-to-End

The vision

Imagine an AI agent that runs your entire sales pipeline autonomously. It identifies potential customers, sends personalised outreach, responds to replies, books discovery calls, drafts proposals based on the conversation, and — when the prospect says yes — sends a contract for signature and closes the deal. No human touches the process between “new lead identified” and “contract signed”.

This isn't science fiction. Every component exists today. The challenge is connecting them into a coherent pipeline. Here's how.

The six-stage pipeline

Each stage has a clear input, a clear output, and a clear tool. Let's walk through the full architecture.

Stage 1: Lead discovery

Goal: Find potential customers who match your ideal customer profile (ICP).

Tools: Apollo.io API, LinkedIn Sales Navigator, Google Maps API, Companies House API, or any enrichment service.

How it works: Define your ICP in structured criteria — industry, company size, role titles, geography. The agent queries enrichment APIs to build a list of leads with names, emails, company info, and context.

const leads = await apollo.search({
  person_titles: ["CTO", "VP Engineering", "Head of Product"],
  organization_num_employees_ranges: ["11,50", "51,200"],
  q_organization_keyword_tags: ["SaaS", "developer tools"],
  person_locations: ["United Kingdom"],
  per_page: 25
});

Output: A list of qualified leads with contact details and company context.

Stage 2: Personalised outreach

Goal: Send a personalised email or LinkedIn message to each lead.

Tools: An LLM (GPT-4, Claude) for copy generation + an email sending service (Resend, SendGrid) or LinkedIn automation tool.

How it works: For each lead, the agent generates a personalised message using their company context, role, and recent activity. No generic templates — every message references something specific about the recipient.

const message = await llm.generate({
  prompt: `Write a 3-sentence cold email to ${lead.name},
  ${lead.title} at ${lead.company}.
  They recently ${lead.recent_activity}.
  We offer API-first e-signing for developer tools.
  Be direct, no fluff, include one clear CTA.`
});

await resend.emails.send({
  from: "alice@startup.com",
  to: lead.email,
  subject: message.subject,
  text: message.body
});

Output: Personalised emails sent to qualified leads.

Stage 3: Meeting booking

Goal: When a lead replies positively, book a discovery call.

Tools: Cal.com API or Calendly API for scheduling + LLM for reply classification and response generation.

How it works: The agent monitors the inbox for replies. When a positive reply comes in, the agent classifies the intent (interested, not interested, question, objection) and responds accordingly. For positive replies, it sends a scheduling link.

const intent = await llm.classify(reply.text, [
  "interested", "not_interested",
  "question", "objection", "out_of_office"
]);

if (intent === "interested") {
  await resend.emails.send({
    to: reply.from,
    subject: "Re: " + reply.subject,
    text: `Great to hear! Here's my calendar:
    https://cal.com/alice/discovery
    Pick any slot that works for you.`
  });
}

Output: Discovery calls booked automatically.

Stage 4: Proposal generation

Goal: After the discovery call, generate a tailored proposal.

Tools: LLM for content generation + your standard pricing/terms.

How it works: The agent takes notes from the discovery call (via transcript or summary), combines them with your standard pricing and terms, and generates a proposal document. The proposal is written in markdown — ready to be sent directly through Signbee for signing if the prospect agrees.

const proposal = await llm.generate({
  prompt: `Generate a project proposal based on:
  - Client: ${lead.company}
  - Need: ${callNotes.summary}
  - Budget discussed: ${callNotes.budget}
  - Timeline: ${callNotes.timeline}
  
  Use our standard pricing:
  - Setup fee: $2,000
  - Monthly: $500/mo
  
  Format as markdown with sections:
  Executive Summary, Scope, Deliverables,
  Timeline, Pricing, Terms.`
});

Output: A tailored proposal document in markdown, ready for review or immediate sending.

Stage 5: Contract and signing

Goal: When the prospect approves the proposal, send a contract for signature.

Tools: Signbee API for e-signing.

How it works: The agent converts the approved proposal into a formal contract (or uses a standard services agreement), and sends it through Signbee for two-party signing. One API call. The prospect gets an email with a signing link. Both parties receive the signed, SHA-256 certified PDF.

const contract = await llm.generate({
  prompt: `Convert this proposal into a formal
  services agreement with standard legal clauses:
  ${proposal}`
});

const result = await fetch("https://signb.ee/api/v1/send", {
  method: "POST",
  headers: {
    "Content-Type": "application/json",
    "Authorization": "Bearer sb_live_..."
  },
  body: JSON.stringify({
    markdown: contract,
    sender_name: "Alice Smith",
    sender_email: "alice@startup.com",
    recipient_name: lead.name,
    recipient_email: lead.email,
    title: `Services Agreement — ${lead.company}`
  })
});

const { document_id } = await result.json();
console.log(`Contract sent: ${document_id}`);

Output: A signing link delivered to the prospect's inbox.

Stage 6: Closed deal

When the prospect signs, both parties receive the certified PDF. The deal is closed. The agent updates the CRM, sends a welcome email, and triggers onboarding workflows. Zero human intervention from lead discovery to signed contract.

The orchestration layer

The six stages need an orchestrator — something that manages state, handles transitions, and decides what to do next. There are three common approaches:

1. Workflow engines (n8n, Temporal, Inngest): Define each stage as a node in a workflow. The engine handles retries, timeouts, and state management. Best for production reliability.

2. Agent frameworks (LangChain, CrewAI, AutoGen): Define each stage as a tool the agent can call. The LLM decides when to transition between stages. Best for flexibility and edge case handling.

3. Simple cron + database: A database table tracks each lead's current stage. A cron job runs every few minutes, checks for leads that need action, and executes the appropriate stage. Best for simplicity and debuggability.

The tool stack

Total cost for a basic setup: under $100/month. That's an autonomous sales agent for less than a single hour of a human SDR's time.

Why the signing step matters most

Every stage before signing is preparation. The signed contract is the only artefact that generates revenue. Without it, you have a pipeline full of conversations and proposals that haven't converted.

This is why the signing step needs to be as frictionless as possible. If the agent generates a proposal and then requires a human to log into DocuSign, create a template, position fields, and send it manually — the pipeline breaks. The autonomy ends at the most important step.

With Signbee, the agent writes the contract in markdown and sends it with one API call. No human intervention. The pipeline stays autonomous end-to-end.

Start small

You don't need to build all six stages at once. Start with stages 5 and 6 — take your existing proposals and automate the contract-sending step. Once that's working, work backwards: automate proposal generation, then meeting booking, then outreach, then lead discovery.

Each stage you automate compounds the ROI. But the signing step — the revenue-generating step — is where you should start.