This is not a tour of what AI can do. Each lesson closes one practical knot where implementation usually gets stuck. Use the guide as a plan for setting it up yourself, the basis for a spec, or a checklist for your contractor.
How an agent differs from a chatbot and which part of the sale is actually worth handing to it.
How to link the model to messages, the deal card, stages, fields, tasks and assigned reps.
What to prepare, how to check your data, connect official WhatsApp and what to do if you're rejected.
Which messages, fields and documents it needs to answer accurately and work across languages.
Test scenarios, quality control, metrics and gradually expanding automation.
You'll learn why a polished answer isn't enough, and which actions turn a plain bot into a participant in the sales process.
The lesson helps you define the agent's job: what it finds out, which data it records, when it changes the stage and the moment it must call in a manager.
In the second lesson you'll walk the whole chain: the customer's message, finding or creating a deal, preparing context, the request to the AI, recording the result and handoff to a manager. Separately — where the business logic should live so that swapping the model doesn't break the process.
Writes wherever it's convenient for them.
Holds the deal, stage, fields, tasks and history.
Builds the request, limits actions, handles errors and handoff to a human.
Understands the message, replies, qualifies and performs permitted actions.
In the guide this diagram is broken down node by node: what receives the message, where state is stored, what data the AI gets, who checks the answer and how actions flow back into Kommo.
Using a pipeline as an example, you'll see exactly what should appear in Kommo after the agent replies: the stage, filled-in data, the next step and a clear point for handing off to a human.
Record the budget, city, dates, the product of interest or any other parameter.
Move the deal after qualification or the customer's reply.
Schedule a call, a document check or the next contact.
Route the customer to the right rep by product, language or region.
Send an agreed reminder if the conversation went quiet.
For many teams this isn't a side step but the main barrier. That's why verification gets its own lesson in the guide: from preparing your data to analysing a rejection and re-applying.
The goal of the lesson: not just to get approved status in Meta, but to set up the channel so the number, Kommo and the AI agent can be joined into one working system.
Check the business details, website, contacts, accounts and the permissions of whoever runs the setup.
Clear up any contradictions in your company details so you don't submit the application blind.
Work out which number to use, what happens to your current WhatsApp and how to link the channel to Kommo.
Identify which node failed the review, fix it and only then submit the application again.
In the fourth lesson you'll decide which messages, fields, documents and external data the agent actually needs. It can take into account the previous conversation, the chosen product, budget, city, dates, the lead source and questions already asked. So it doesn't restart the conversation or make the customer repeat what they've already said.
The result: the conversation feels like a continuation of dealing with your company, not a fresh encounter with yet another bot.
Replies without a wait, clarifies the key parameter, shows suitable options and records the result in Kommo.
In the morning the manager sees a deal already started and qualified, not an eight-hour-old "still interested?" message.
Understands the message, replies in the customer's language and saves the translation in the deal card for the team.
The company serves a new market without a separate support line and without losing control of the conversation.
By a Kommo rule, it sends a fitting reminder that takes the previous conversation into account, and brings the customer who replies back into the pipeline.
Follow-up no longer depends on a manager's memory. A silent customer no longer means an automatically lost deal.
The final lesson ends with a measurement table. Before launch you record your baseline numbers, and afterwards you compare the changes in response speed, qualification quality and handoff to a manager.
How long the customer waits before a real conversation begins.
How many inquiries reach the manager already with the data they need.
How many conversations the manager has to start over or re-clarify.
We implement Kommo, integrations and AI agents for companies that sell through chat. In practice the problem is rarely in the model itself. It shows up at the seams: the channel doesn't match the deal, the agent gets no context, actions aren't controlled, and handoff to a manager happens too late.
That's why the guide isn't about prompts or picking the "smartest" AI. It closes five knots in order: the agent's role, Kommo, Meta and WhatsApp, context, launch and measurement.
The guide is there so you understand the architecture and limits and can tell a working solution from a pretty demo. But you don't have to walk the whole path alone. We can take the build off your hands.
We decide what the agent should do itself, which data to collect and at which stage to hand the deal to a manager.
We check your data and access, guide the verification, the business account and number connection. The final approval decision is Meta's.
We link messages, the deal card, fields, stages, tasks and your chosen model or n8n scenario.
We define the knowledge sources, permitted actions, prohibitions, languages and the conditions for handing a conversation to a human.
We check real scenarios, fix failures, train the team and record the metrics we'll use to judge the result.
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