Nikolay Sekachev AI Agent for Operations
Connect an AI agent to documents, policies, and systems. From LLM fundamentals to workflow automation and quality control.
Offline intensive · 2 modules × 4 academic hours 1 week · 2 offline sessions Operations managers, analysts, and functional leaders
Who it is for
You already have an AI agent installed (OpenClaw, Claude Code, or similar). Now you need to make it work with internal processes: document flow, approvals, reporting, and quality control.
No coding is required. But understanding how the agent works is required, otherwise each failure will depend on IT support.
Why the fundamentals matter
An agent is not a magic button. It is a three-layer system: language model, tools, and decision loop. Once you understand this architecture, you can diagnose any issue and configure any tool, current or future.
What you will get
- Architecture understanding: LLM -> Agent -> Agentic AI, and why this matters in operations.
- An agent connected to your documents and systems through MCP.
- Configured workflows for repetitive operational tasks.
- A practical skillset for debugging and scaling automation.
Module 1. Fundamentals and documents (4 academic hours)
Block 1. How the agent works - three layers
- LLM: tokens, context window, prompt engineering; why prompt precision defines result precision.
- Agent = LLM + Tools + Loop: ReAct loop, tool integration, when the agent “thinks” vs “acts”.
- Agentic AI: self-configuring systems (OpenClaw, ClawHub), skills, and automatic data source wiring.
Block 2. Connecting documents and systems
- Hands-on: the agent reads, analyzes, and extracts data from documents through MCP.
- Working with spreadsheets, PDFs, internal databases, and file storages.
- Knowledge base structure: how to organize data so the agent can find what it needs.
Block 3. Request handling and approvals
- Automatic routing of requests by rules and priority.
- Skills for document completeness checks.
- Hands-on: configure an agent for one real approval workflow.
Block 4. Reporting and monitoring
- Agent analyst: automatic summaries from tables and systems.
- Report templates and anomaly alerts.
- Weekly assignment: launch the agent on one operational workflow.
Module 2. Debugging and scaling (4 academic hours)
Block 5. Results review
- What worked, what broke, and how to explain it via fundamentals (context, tools, loop).
- Agent error analysis and skills adjustments.
- Edge cases: non-standard documents and requests.
Block 6. Quality control and compliance
- Agent as controller: document checks against standards.
- Automated audits: detect mismatches and omissions.
- Human-in-the-loop: when and how to involve people.
Block 7. Multi-step workflows
- Task chains: how an agent executes multi-stage processes.
- Exception handling and rollback patterns.
- Hands-on: configure an end-to-end workflow from request to closure.
Block 8. Scaling
- Add new workflows without breaking existing ones.
- Efficiency metrics: what to measure and how to estimate ROI.
- From one workflow to a department-level operational AI 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.