Nikolay Sekachev Nikolay Sekachev
EN ET RU

AI Agent in Sales and Customer Service

Connect an AI agent to CRM, requests, and customer data. From understanding LLM behavior to a configured sales pipeline.

Offline intensive · 2 modules × 4 academic hours 1 week · 2 offline sessions Sales managers, account managers, and support teams

Who it is for

You already have an AI agent installed (OpenClaw, Claude Code, or similar). Now you need to make it work with your data and processes: qualify leads, process requests, and support client communications.

You are not a programmer. But you understand your sales process better than anyone else, which is why only you can configure the agent to work in real operations.

Why the fundamentals matter

Tools change every few months. OpenClaw, Claude, GPT are different wrappers around the same principles. We do not teach button-clicking, we teach how the agent thinks, so you can configure whatever tool appears next.

What you will get

  • Understanding of three levels: LLM -> Agent -> Agentic AI and how each impacts quality.
  • A configured agent connected to your data and CRM.
  • Working prompts and skills for sales and support scenarios.
  • Practical diagnosis and tuning skills without IT dependency.

Module 1. Fundamentals and data (4 academic hours)

Block 1. How the agent works - three levels

  • LLM: tokens, context, temperature; why agents may hallucinate and how to control it.
  • Agent = LLM + Tools + Loop: how a prompt turns into action, what the ReAct loop is, and why human-in-the-loop matters.
  • Agentic AI: multi-agent systems (OpenClaw, ClawHub) that can connect skills and tools.

Block 2. Connecting customer data

  • Hands-on: connect the agent to CRM, spreadsheets, and request databases through MCP.
  • Agent context: what data to provide, what to exclude, and how to structure the knowledge base.
  • Security: personal data, access limits, and action audits.

Block 3. Lead qualification and request processing

  • Skills for automatic qualification of inbound leads.
  • Prioritization: how the agent ranks requests by urgency and value.
  • Hands-on: configure a pipeline from incoming request to manager-ready response.

Block 4. First automations

  • Auto-replies and template communication through the agent.
  • FAQ bot based on your knowledge base.
  • Weekly assignment: launch one real process and collect results.

Module 2. Debugging and scaling (4 academic hours)

Block 5. Results review

  • Analyze what worked, what broke, and why.
  • Typical error patterns and fixes through fundamentals (context, tokens, prompt).
  • Tune skills and prompts using real data.

Block 6. Advanced scenarios

  • Handling objections and non-standard requests.
  • Multi-step dialogues with clarifying questions and branching.
  • Escalation: how the agent routes complex cases to humans.

Block 7. Reporting and analytics

  • Agent analyst: automatic funnel reports and request pattern insights.
  • Dashboards and real-time alerts.
  • How to read agent logs and explain decision paths.

Block 8. Scaling

  • Add new scenarios without breaking existing ones.
  • Quality checklist: when the agent is ready for lower-supervision use.
  • From one workflow to a full department automation system.

Format

  • Offline live sessions with practical work on participants’ real data.
  • 2 modules of 4 academic hours within one calendar week.
  • Practical assignment between modules on real workflows.
  • Groups up to 12 participants.