Nikolay Sekachev AI Agent in Marketing and SMM
Connect an AI agent to your content, brand, and channels. From LLM principles to automated content production.
Offline intensive · 2 modules × 4 academic hours 1 week · 2 offline sessions Marketers, SMM specialists, and content 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 content, brand, and channels: generate materials, adapt for platforms, and track performance metrics.
You know your audience and tone of voice better than any model. The goal is to transfer this expertise to the agent and turn it into a reliable content system.
Why the fundamentals matter
Midjourney, Suno, and Remotion change every few months. But generation fundamentals (diffusion, tokenization, prompt architecture) remain. We teach the fundamentals so you can master new tools in hours, not weeks.
What you will get
- Understanding of three levels: LLM -> Agent -> Agentic AI and content applications.
- An agent that produces materials in your brand voice.
- Working skills and a pipeline for consistent content output.
- Practical control over quality and scaling.
Module 1. Fundamentals and content (4 academic hours)
Block 1. How the agent works - three levels
- LLM: tokens, context, temperature; why the same prompt may produce different outcomes and how to control it.
- Agent = LLM + Tools + Loop: from text generation to content pipeline generation (idea -> draft -> editing -> publishing).
- Agentic AI: self-configuring systems (OpenClaw, ClawHub), content skills, and automatic channel connections.
Block 2. Brand knowledge base
- How to describe tone of voice, values, and constraints so the agent follows them.
- Markdown knowledge base structure: what to include and how to organize it.
- Hands-on: create a brand knowledge base and test agent usage.
Block 3. Content generation
- Skills for posts, articles, newsletters, and product descriptions.
- Channel adaptation: one source piece -> five formats.
- Visuals: prompts for image generation, banner and card templates.
Block 4. Content pipeline
- Automating the cycle: idea -> draft -> editing -> publishing.
- Publication calendar via agent.
- Weekly assignment: launch the agent for one real content channel.
Module 2. Debugging and scaling (4 academic hours)
Block 5. Results review
- Analyze generated content: what worked and what did not.
- Tune skills and prompts using real feedback.
- A/B approach: testing content variants through the agent.
Block 6. Analytics and insights
- Agent analyst: automatic channel metrics collection.
- Pattern detection: what content performs and why.
- Competitive monitoring and benchmarking with AI.
Block 7. Scaling
- Multi-channel: one brief, content for all platforms.
- Batch production: more output without quality loss.
- Localization and adaptation for different audiences.
Block 8. System
- Editorial control: quality checklists for AI content.
- Agent integration into existing team workflow.
- From content assistant to full AI editorial operation.
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 work tasks.
- Groups up to 12 participants.