Equinet Academy
3-Hour Intro to OpenClaw Autonomous AI Agent Deployment Workshop
Workshop Description
Most AI agent platforms are hosted, subscription-based, and limited by vendor rules. OpenClaw introduces a different model: a free, open-source autonomous AI agent that can be deployed on your own infrastructure, connected to your preferred LLM backend, and operated through a messaging platform such as Telegram.
This 3-hour introductory workshop introduces learners to the OpenClaw agent model and the fundamentals of self-hosted AI agent deployment. Participants will explore how autonomous agents receive instructions, plan tasks, use tools, and return outputs through a chat-based interface.
The focus is on architecture, workflow, and practical use case understanding, not full production deployment in one session. Learners will examine how OpenClaw connects LLMs, tools, instruction files, memory, and Telegram into a working automation system, and how this differs from commercial agent platforms.
Why Attend This Workshop?
✔ Understand what OpenClaw is and how self-hosted AI agents work
✔ Analyse how autonomous agents plan, use tools, and complete multi-step tasks
✔ Assess the difference between commercial agent platforms and open-source deployment
✔ Examine how LLM backends, Telegram, tools, and instruction files connect
✔ Understand suitable business use cases for self-hosted AI automation
Workshop Outline
- Understand what OpenClaw is and why self-hosted AI agents matter
- Explore the basic architecture of an autonomous AI agent
- Compare OpenClaw with commercial platforms such as ChatGPT Agents, Claude, n8n, Make, and Zapier
- Examine how LLM backends such as OpenAI, Claude, Gemini, or local models connect to OpenClaw
- Understand how Telegram functions as the agent interface
- Identify how tools, skills, memory, and instruction files shape agent behaviour
- Evaluate real business use cases for OpenClaw automation
Workshop Highlights
Phase 1 — OpenClaw and Autonomous Agent Foundations
✔ Understand how OpenClaw processes user instructions through an LLM backend
✔ Examine the difference between chatbot responses and autonomous task execution
✔ Identify where OpenClaw fits within the broader AI agent ecosystem
✔ Understand why self-hosting changes cost, control, customisation, and vendor dependence
Phase 2 — Agent Configuration and Interaction Logic
✔ Explore how Telegram becomes the operational interface for the agent
✔ Understand the role of API keys and LLM backend selection
✔ Examine the four instruction files: AGENTS.md, SOUL.md, USER.md, and IDENTITY.md
✔ Recognise how tools, memory, and Skills Hub capabilities extend what the agent can do
Phase 3 — Business Automation and Deployment Awareness
✔ Identify practical use cases such as research, content workflows, lead triage, and customer communication
✔ Understand common agent failure modes such as poor instructions, API issues, tool misuse, and unreliable outputs
✔ Examine what production deployment involves: hosting, access control, logging, monitoring, and updates
✔ Understand how the full course develops a working Telegram-based OpenClaw agent deployment
Who Should Attend
- Developers and software engineers exploring open-source AI automation
- IT professionals and system administrators responsible for deploying AI tools
- Technical entrepreneurs and SME owners who want control over their AI agent stack
- AI practitioners who have completed an Agentic AI course and want deeper tool-specific implementation
- Data engineers and automation specialists building internal or client workflows
- Learners evaluating whether OpenClaw is suitable before committing to the full deployment course
Pre-requisites
- Basic understanding of AI agents is helpful
- Basic familiarity with APIs and API keys is recommended
- Some comfort with command-line tools is useful
- Telegram account is recommended for learners who want to follow the workflow context
- No full server setup is required for the preview workshop
What You’ll Walk Away With
- Clear understanding of what OpenClaw is
- Basic autonomous agent architecture lens
- Awareness of how LLMs, Telegram, tools, skills, and instruction files connect
- Understanding of the difference between hosted and self-hosted agent platforms
- Practical view of OpenClaw business use cases
- Readiness checklist for the full 2-day technical deployment course
- Featured in Straits Times, Channel 5, CNA, and Lian He Zao Bao as the founder of local DTC fashion brand Wetteeshirt.
- Built KLARA, an AI-powered Primary Math platform in Singapore
- ACLP (Advanced Certificate in Learning and Performance) Certified Trainer
Frequently Asked Questions (FAQs)
For the preview workshop, no coding is required. However, the full course requires comfort with command-line interfaces, configuration files, APIs, and server environments.
No. This is a 3-hour preview workshop. The focus is on understanding the architecture, workflow, and business use cases. The full 2-day course covers local setup, cloud deployment, Telegram integration, Skills Hub, custom tools, and capstone build.
An Agentic AI course usually gives broad exposure to commercial platforms and no-code automation tools. This workshop introduces one specific open-source, self-hosted agent framework: OpenClaw.
Telegram is the main platform introduced because the full course uses Telegram for agent interaction, testing, and demonstration.
Yes, at an introductory level. The workshop explains how OpenAI, Claude, Gemini, or local models can act as the LLM backend, but detailed configuration is covered in the full course.
OpenClaw can support research workflows, content production pipelines, lead triage, customer communication, document handling, API-based automation, and internal task execution.
This workshop introduces OpenClaw’s architecture, messaging interface model, LLM backend logic, instruction files, tools, and business use cases. The full course extends into hands-on setup, Telegram bot connection, Skills Hub installation, custom API integration, debugging, production deployment, workflow skill building, and a live capstone agent demonstration.
