AI Agents Training in Tashkent

This instructor-led, live training in Tashkent (online or onsite) is aimed at advanced-level AI engineers, robotics developers, and automation specialists who wish to leverage DeepSeek for building intelligent AI agents and autonomous systems.

By the end of this training, participants will be able to:

• Understand the architecture and capabilities of DeepSeek AI models.
• Integrate DeepSeek into AI agents for decision-making and automation.
• Apply reinforcement learning techniques for training autonomous systems.
• Deploy AI-driven autonomous agents in real-world environments.

Autonomous AI agents are rapidly being integrated into decision-making processes across sectors such as defense, healthcare, finance, and infrastructure. As these systems gain greater independence and autonomy, there is an urgent need for robust ethical frameworks, governance mechanisms, and regulatory oversight to guide their design and deployment.

This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to explore the ethical, societal, and regulatory implications of deploying autonomous AI agents in real-world environments.

By the end of this training, participants will be able to:

• Analyze the ethical risks and moral dilemmas posed by autonomous AI agents.
• Evaluate governance frameworks and regulatory models relevant to high-stakes AI deployment.
• Assess accountability, explainability, and transparency mechanisms in autonomous systems.
• Develop strategies for aligning AI agent behavior with legal, ethical, and societal values.

Format of the Course

• Interactive lecture and discussion.
• Case studies and role-playing simulations.
• Hands-on analysis of governance models and ethical frameworks.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

AI-powered automation merges the adaptability of no-code platforms with the cognitive capabilities of large language models and autonomous agents to develop scalable, intelligent systems.

This instructor-led, live training (available online or on-site) targets intermediate-level professionals aiming to design, construct, and optimize automation pipelines equipped with AI-driven decision-making, orchestration, and self-execution functionalities.

Upon completion of this training, participants will be able to:

• Design no-code automation workflows augmented by AI interpretation and response mechanisms.
• Utilize prompt chaining, webhook triggers, and APIs to construct scalable logic flows.
• Develop intelligent applications that autonomously connect services and interpret data.
• Understand, design, and deploy AI agents capable of goal-oriented action planning and execution.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practice sessions.
• Hands-on implementation in a live-lab environment.

Customization Options

• For customized training requests, please contact us to arrange.

This instructor-led, live training in Tashkent (online or onsite) is aimed at intermediate-level AI developers, security specialists, and compliance officers who wish to design and implement secure AI agents while addressing ethical concerns and robustness.

By the end of this training, participants will be able to:

• Understand the security risks and ethical challenges in AI agent development.
• Implement security-first design principles for AI models.
• Apply adversarial robustness techniques to prevent attacks on AI agents.
• Ensure compliance with ethical AI guidelines and regulatory standards.

Edge & Lightweight Agents is a practical course focused on deploying agentic AI workloads on resource-constrained devices. Participants learn to build, optimize, and manage lightweight agents capable of local reasoning and inference, improving speed, privacy, and reliability in distributed environments. The course emphasises performance tuning, low-latency design, and hardware–software integration.

This instructor-led, live training (online or on-site) is aimed at intermediate-level professionals who wish to implement and optimise on-device agentic systems using Python and edge AI frameworks.

By the end of this training, participants will be able to:

• Understand the architecture and challenges of running agentic AI on edge devices.
• Design lightweight agent loops suitable for constrained environments.
• Implement local inference using TensorFlow Lite, PyTorch Mobile, and ONNX.
• Integrate agents with sensors, actuators, and IoT platforms.
• Optimise performance, energy use, and latency for real-time operation.

Format of the Course

• Interactive lecture and practical demonstrations.
• Hands-on development in local or emulated environments.
• Project-based learning and guided implementation exercises.

Course Customisation Options

• To request a customised training for this course, please contact us to arrange.

This instructor-led, live training in Tashkent (online or onsite) is designed for intermediate to advanced AI developers, researchers, and multimedia engineers looking to build AI agents that can understand and generate multi-modal content.

Upon completion of this training, participants will be able to:

• Create AI agents that process and integrate text, image, and speech data.
• Implement multi-modal models like GPT-4 Vision and Whisper ASR.
• Optimize multi-modal AI pipelines for better efficiency and accuracy.
• Deploy multi-modal AI agents in real-world applications.

This instructor-led, live training in Tashkent (online or onsite) is aimed at beginner-level to intermediate-level developers and designers who wish to understand and build AI-powered personal assistants for various use cases.

By the end of this training, participants will be able to:

• Understand the core principles of AI-powered personal assistants.
• Learn the fundamentals of integrating language models with smart assistants.
• Build and customize a personal assistant using AI frameworks and tools.
• Deploy personal assistants for real-world applications in different industries.

This instructor-led, live training in Tashkent (online or onsite) is designed for advanced-level AI engineers, enterprise software developers, and business leaders who aim to customise and deploy LLM-based AI agents for enterprise applications.

By the end of this training, participants will be able to:

• Understand the architecture and capabilities of open-source LLMs.
• Customise and fine-tune LLMs for enterprise use cases.
• Deploy AI agents using LangChain and Hugging Face.
• Integrate LLM-powered agents into business workflows.

This instructor-led, live training in Tashkent (available online or on-site) is designed for advanced-level robotics engineers, AI researchers, and automation specialists who aim to develop AI-driven autonomous robotic systems capable of handling complex tasks and facilitating human-AI collaboration.

By the end of this training, participants will be able to:

• Understand the role of AI agents in robotic decision-making and automation.
• Implement AI-driven navigation and obstacle avoidance.
• Develop human-AI collaborative robotic systems.
• Deploy AI-powered perception and control systems in robots.

This course delves into the design, coordination, and implementation of multi-agent systems (MAS) utilizing Python. Participants will acquire the skills to construct agents that communicate, collaborate, and adapt to achieve collective goals within complex and dynamic environments.

This instructor-led, live training (available online or onsite) is targeted at advanced-level professionals seeking to design and implement multi-agent systems for intelligent automation, simulation, and decision-making applications.

Upon completion of this training, participants will be able to:

• Grasp the architecture and fundamental principles of multi-agent systems.
• Develop agents capable of effective communication, coordination, and negotiation.
• Implement distributed environments for agent interactions.
• Apply reinforcement learning and planning techniques in multi-agent contexts.
• Simulate both cooperative and competitive agent behaviors.
• Design hybrid workflows that integrate humans and intelligent agents.

Course Format

• Instructor-led lectures and live demonstrations.
• Hands-on exercises utilizing open-source agent frameworks.
• A practical group project simulating a multi-agent scenario.

Course Customization Options

• To request customized training for this course, please contact us to arrange.

This instructor-led, live training in Tashkent (online or on-site) is designed for intermediate-level financial professionals, risk analysts, and AI engineers who wish to develop and deploy AI-driven solutions for financial automation and fraud detection.

By the end of this training, participants will be able to:

• Understand the role of AI in financial automation and fraud detection.
• Build AI models to detect fraudulent transactions.
• Apply machine learning techniques for real-time risk assessment.
• Deploy AI-powered financial monitoring systems.

This instructor-led, live training in Tashkent (online or onsite) is aimed at beginner-level to intermediate-level customer service professionals, chatbot developers, and automation teams who wish to implement AI-driven solutions for customer support and service automation.

By the end of this training, participants will be able to:

• Understand the fundamentals of AI-powered customer support.
• Develop and integrate AI chatbots for handling customer queries.
• Leverage NLP and machine learning to enhance chatbot interactions.
• Deploy AI agents in real-world customer service applications.

This instructor-led, live training in Tashkent (online or onsite) is tailored for intermediate to advanced healthcare professionals and AI developers seeking to deploy AI-driven healthcare solutions.

Upon completion of this training, participants will be able to:

• Understand the role of AI agents in healthcare and diagnostics.
• Develop AI models for medical image analysis and predictive diagnostics.
• Integrate AI with electronic health records (EHR) and clinical workflows.
• Ensure compliance with healthcare regulations and ethical AI practices.

This instructor-led, live training in Tashkent (online or onsite) is aimed at beginner-level to intermediate-level AI developers and UI/UX designers who wish to learn how to build, optimize, and deploy AI-powered virtual assistants.

By the end of this training, participants will be able to:

• Design effective conversational AI workflows and UI/UX interfaces.
• Implement AI-driven natural language processing (NLP) models.
• Integrate AI assistants with APIs and third-party platforms.
• Deploy and maintain AI assistants for real-world applications.

Mistral AI is a powerful family of open-source and enterprise-ready AI models designed for language, multimodal, and agentic applications.

This instructor-led, live training (available online or on-site) is tailored for intermediate to advanced-level professionals who aim to build, deploy, and manage AI agents using Mistral's Medium 3, Le Chat Enterprise, and Devstral models.

By the end of this training, participants will be able to:

• Comprehend the architecture and capabilities of Mistral Medium 3, Le Chat Enterprise, and Devstral.
• Design and implement AI agents that leverage Mistral models for enterprise and developer use cases.
• Integrate coding systems, connectors, and enterprise data into agent workflows.
• Optimize performance, cost efficiency, and compliance for Mistral-powered agents.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical sessions.
• Hands-on implementation within a live-lab environment.

Course Customization Options

• To request a customized version of this course, please contact us to arrange it.

Python is the core language powering the development and orchestration of autonomous AI agents. This course focuses on practical implementation using modern SDKs and frameworks such as LangChain and AutoGen to build, connect, and manage agent workflows.

This instructor-led, live training (online or onsite) is aimed at intermediate-level backend engineers, platform engineers, and ML engineers who wish to implement and orchestrate autonomous agents using Python tooling and APIs.

By the end of this training, participants will be able to:

• Set up and configure Python-based environments for agentic systems.
• Use popular agent SDKs like LangChain and AutoGen to create functional agents.
• Integrate tools and APIs to extend agent capabilities.
• Orchestrate multi-agent workflows and communication patterns.
• Apply best practices for debugging, testing, and maintaining agentic codebases.

Format of the Course

• Interactive lecture and discussion.
• Hands-on programming exercises and live demos.
• Practical projects building end-to-end agent workflows.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This course imparts practical engineering skills for designing, building, testing, and deploying agentic (autonomous) systems using Python. Key topics include the agent loop, tool integrations, memory and state management, orchestration patterns, safety controls, and production-ready considerations.

This instructor-led live training is available online or on-site. It is designed for intermediate to advanced ML engineers, AI developers, and software engineers looking to construct robust, production-ready autonomous agents with Python.

Upon completing this training, participants will be capable of:

• Designing and implementing the agent loop and decision-making workflows.
• Integrating external tools and APIs to expand agent capabilities.
• Implementing short-term and long-term memory architectures for agents.
• Coordinating multi-step orchestrations and ensuring agent composability.
• Applying best practices for safety, access control, and observability in deployed agents.

Course Format

• Interactive lectures and discussions.
• Hands-on labs for building agents using Python and popular SDKs.
• Project-based exercises resulting in deployable prototypes.

Course Customization Options

• For customized training requests, please contact us to make arrangements.

This instructor-led, live training in Tashkent (online or onsite) is aimed at advanced-level professionals who wish to master the techniques for deploying and managing AI agents in production environments.

By the end of this training, participants will be able to:

• Design and implement scalable AI deployment pipelines.
• Use tools like Docker and Kubernetes to containerize and orchestrate AI agents.
• Monitor and optimize AI agents in production environments.
• Implement CI/CD workflows for AI agent deployments.
• Ensure compliance with security and data governance requirements.

This instructor-led, live training in Tashkent (online or onsite) is aimed at advanced-level AI professionals who wish to master reinforcement learning techniques and implement them for training AI agents in solving complex problems.

By the end of this training, participants will be able to:

• Understand the core principles of reinforcement learning and Markov Decision Processes (MDPs).
• Design and implement RL algorithms such as Q-Learning, SARSA, and Deep Q-Networks (DQN).
• Utilize frameworks like OpenAI Gym and RL libraries for practical applications.
• Train AI agents to solve real-world, multi-step decision-making problems.
• Address challenges such as exploration-exploitation trade-offs and convergence in RL training.

This instructor-led, live training in Tashkent (online or on-site) is designed for intermediate-level AI developers and automation specialists who wish to integrate agentic capabilities into AI-powered applications.

By the end of this training, participants will be able to:

• Understand the principles of agentic AI and autonomous decision-making.
• Implement goal-driven AI agents using self-optimisation techniques.
• Integrate multi-agent collaboration to solve complex problems.
• Enhance AI-human interaction through adaptive user experiences.
• Deploy agentic AI models in real-world applications.

This instructor-led, live training in Tashkent (online or onsite) is designed for advanced AI professionals who wish to master the skills to design, build, and deploy MAS that solve complex, real-world problems.

By the end of this training, participants will be able to:

• Understand the principles of multi-agent system architectures.
• Implement strategies for communication, coordination, and decision-making in MAS.
• Apply game theory to model agent interactions and resolve conflicts.
• Leverage frameworks like JADE to create scalable MAS solutions.
• Address challenges like scalability, trust, and emergent behavior in MAS.

This instructor-led, live training in Tashkent (online or onsite) is designed for advanced-level professionals seeking to develop and optimize multi-agent systems using Agentic AI frameworks.

By the end of this training, participants will be able to:

• Grasp the core principles of Agentic AI within multi-agent environments.
• Build AI-driven agents capable of autonomous interaction.
• Implement reinforcement learning techniques to foster adaptive AI behavior.
• Enhance collaboration and manage competition among multiple agents.
• Apply Agentic AI solutions in robotics, gaming, and enterprise automation.

This instructor-led, live training in Tashkent (online or on-site) is aimed at intermediate-level business analysts, RPA developers, and AI professionals who wish to harness the power of AI agents for business process automation.

By the end of this training, participants will be able to:

• Understand the role of AI agents in business process automation.
• Identify opportunities for automation in key business domains.
• Develop AI-driven solutions for customer support, finance, and supply chain workflows.
• Integrate AI agents with existing RPA and business systems.
• Evaluate and optimize AI automation strategies for efficiency.

Vertex AI Agent Builder offers a no-code or low-code platform for creating grounded agents that merge generative models with Retrieval-Augmented Generation (RAG). This enables teams to swiftly construct agents that leverage enterprise data and search capabilities to deliver precise, context-aware responses.

This instructor-led training, available both online and onsite, is designed for intermediate-level professionals looking to design, configure, and deploy smart agents utilizing Vertex AI Agent Builder and RAG patterns.

Upon completing this training, participants will gain the ability to:

• Design grounded agent workflows using Agent Builder.
• Implement RAG pipelines utilizing search and vector stores.
• Securely integrate enterprise data sources for retrieval purposes.
• Evaluate and refine agent behavior through testing and performance metrics.

Course Format

• Interactive lectures and discussions.
• Practical labs using Vertex AI Agent Builder and RAG components.
• Project-based exercises focused on building and refining agents.

Customization Options

• For customized training arrangements, please contact us.

This instructor-led, live training in Tashkent (available online or on-site) is designed for intermediate-level game developers and AI enthusiasts who aim to effectively integrate AI agents into gaming applications.

By the end of this training, participants will be able to:

• Understand the role of AI agents in modern gaming.
• Develop decision-making systems using decision trees and finite state machines.
• Implement pathfinding algorithms such as A* for in-game navigation.
• Apply reinforcement learning techniques to create adaptive AI behaviours.
• Optimise AI performance for real-time gaming environments.

Large language models (LLMs) and autonomous agent frameworks such as AutoGen and CrewAI are transforming how DevOps teams automate tasks like change tracking, test generation, and alert triage by mimicking human collaboration and decision-making.

This instructor-led, live training (available online or onsite) is designed for advanced-level engineers who aim to design and implement DevOps automation workflows driven by large language models (LLMs) and multi-agent systems.

By the conclusion of this training, participants will be able to:

• Integrate LLM-based agents into CI/CD workflows for intelligent automation.
• Automate test generation, commit analysis, and change summaries using agents.
• Coordinate multiple agents to triage alerts, generate responses, and offer DevOps recommendations.
• Construct secure and maintainable agent-powered workflows using open-source frameworks.

Format of the Course

• Interactive lecture and discussion.
• Ample exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in Tashkent (online or onsite) is aimed at advanced-level professionals who wish to leverage Agentic AI for decision-making in complex business and technical scenarios.

By the end of this training, participants will be able to:

• Understand the principles of autonomous decision-making in AI.
• Design and implement AI agents that operate with minimal human intervention.
• Integrate Agentic AI into automation workflows and business systems.
• Optimize AI-driven decision processes for efficiency and scalability.
• Ensure compliance, security, and ethical considerations in AI autonomy.

This instructor-led live training, delivered in Tashkent (online or onsite), is tailored for intermediate-level professionals seeking to deepen their expertise in designing and developing autonomous agents for practical applications.

Upon completion, participants will be able to:

• Comprehend the foundational concepts of autonomous agents.
• Explore real-world applications of autonomous AI agents.
• Design, train, and implement agents using reinforcement learning.
• Integrate agents into existing systems for automation and decision-making.
• Address ethical considerations and challenges in deploying autonomous agents.

Devstral is an open-source framework purpose-built for creating and operating coding agents capable of interacting with codebases, developer tools, and APIs to boost engineering productivity.

This instructor-led live training (available online or onsite) targets intermediate to advanced ML engineers, developer-tooling teams, and SREs aiming to design, implement, and optimize coding agents using Devstral.

Upon completing this training, participants will be able to:

• Configure and set up Devstral for coding agent development.
• Design agentic workflows for exploring and modifying codebases.
• Integrate coding agents with developer tools and APIs.
• Apply best practices for secure and efficient agent deployment.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical sessions.
• Hands-on implementation within a live lab environment.

Customization Options

• To arrange a tailored version of this course, please contact us.

This instructor-led, live training in Tashkent (available online or on-site) is designed for beginner-level professionals who wish to understand how AI agents function and build their own interactive chatbot systems.

By the end of this training, participants will be able to:

• Understand the fundamentals of AI agents and their applications.
• Explore the core principles of natural language processing (NLP).
• Design a conversational flow for a chatbot.
• Develop a functional chatbot using Python and related tools.
• Deploy the chatbot to interact with real users.

This instructor-led, live training in Tashkent (online or onsite) is targeted at beginner-level professionals who aim to comprehend and develop simple AI agents for real-world applications.

Upon completing this training, participants will be capable of:

• Grasping the fundamental concepts of AI agents.
• Recognizing various types of AI agents and their respective uses.
• Designing and implementing basic AI agents for practical tasks.
• Exploring tools and frameworks used in building AI agents.

This instructor-led live training in Tashkent (online or onsite) is designed for beginner to intermediate developers and cloud practitioners who wish to utilize Alibaba Cloud to build AI agents that can automate tasks, answer queries, and integrate with business systems.

By the end of this training, participants will understand AI agent architecture on Alibaba Cloud, construct a simple agent workflow, link an agent to enterprise knowledge and tools, and deploy and monitor the agent in a cloud environment.

This instructor-led, live training (available online or on-site) is designed for beginner-level professionals who wish to understand and create simple AI agents for real-world applications.

By the end of this training, participants will be able to:

• Understand the fundamental concepts of AI agents.
• Identify different types of AI agents and their applications.
• Design and implement basic AI agents for practical tasks.
• Explore tools and frameworks for building AI agents.
• Explore different use cases suitable for learning AI.

This advanced, hands-on seminar is tailored for software engineers, architects, technical leads, engineering managers, and delivery teams seeking to comprehend how Artificial Intelligence can expedite the entire software delivery lifecycle, ranging from requirements analysis to the generation of production-ready code.

This instructor-led, live training in Tashkent (online or in-person) is tailored for developers and technical professionals at beginner, intermediate, and advanced levels who wish to utilize Microsoft Azure to build, test, and deploy AI agents for business applications.

By the end of this training, participants will be able to: understand AI agent architecture on Azure, create and configure a functional agent, connect agents to business knowledge sources, and evaluate and prepare agents for deployment.

This instructor-led live training, conducted Tashkent (online or onsite), targets software developers and DevOps teams who want to utilize OpenAI Codex CLI to automate coding tasks, perform code reviews, and execute multi-step workflows via the terminal.

By the end of the course, participants will be capable of installing and configuring Codex CLI, executing tasks through natural language prompts, managing approval modes, and integrating the tool with Git and CI pipelines.

This instructor-led, live training (Tashkent online or onsite) is aimed at developers who wish to use GitHub Copilot Agent Mode to autonomously build features, run tests, and manage larger coding tasks.

By the end of this training, participants will be able to activate Agent Mode, plan and iterate within the agent loop, execute terminal commands, and implement enterprise governance.

This instructor-led, live training in Tashkent (online or onsite) is aimed at developers and leads who wish to use Cursor to build features faster, refactor across files, and manage team-wide AI workflows.

By the end of this training, participants will be able to navigate using Composer and agent mode, target code with context-aware chat, deploy team workspaces, and integrate Cursor with version control.

This instructor-led, live training in Tashkent (online or onsite) is designed for intermediate-level solution architects, AI engineers, developers, and technical product teams who intend to use Tencent ADP to build enterprise AI agents incorporating RAG, workflow automation, and operational guardrails.

By the end of this training, participants will be able to design ADP-based agents, create knowledge pipelines, orchestrate workflows and multi-agent patterns, and implement production-ready guardrails.

This instructor-led, live training in Tashkent (online or onsite) is aimed at beginner-level / intermediate-level / advanced-level developers and technical practitioners who wish to use Google Cloud to build AI agents that can automate tasks, use tools, and work with business data.

By the end of this training, participants will be able to: explain AI agent concepts, build agents with Vertex AI, connect agents to external tools and data, and deploy agents for business use cases.

This instructor-led, live training in Tashkent (online or onsite) is aimed at developers and DevOps professionals who wish to use Hermes Agent to automate development tasks, manage infrastructure, and build persistent AI workflows.

By the end of this training, participants will be able to install and configure Hermes Agent, interact with it across multiple platforms, use 40+ built-in tools, and deploy on various backends.

This instructor-led, live training in Tashkent (online or onsite) is aimed at business users, operations teams, and technical professionals who wish to use Hermes Agent to automate work, create reusable AI-driven processes, and improve team productivity.

By the end of this training, participants will be able to: explain Hermes Agent basics, set up and use core features, build practical workflows, and apply good operational practices.

This instructor-led, live training in Tashkent (available online or on-site) is intended for developers who wish to use Hermes Agent to design and operate secure local AI workflows for code assistance.

By the end of this training, participants will be able to: explain Hermes Agent concepts, configure secure local agent environments, build practical coding workflows, and apply safety and governance controls.

This instructor-led, live training in Tashkent (online or onsite) is aimed at developers and technical teams who wish to use Ollama and open models to build and run private agentic AI solutions on internal infrastructure.

By the end of this training, participants will be able to: install and configure Ollama, evaluate and run open models locally, create simple agentic and retrieval-based workflows, and apply security and governance controls for regulated environments.

This program offers a practical, hands-on methodology for creating local AI and LLM-driven applications using LangChain and CrawAI. Learners will first examine data analytics, machine learning, and model training strategies before progressing to the development of LLM-based solutions.

The curriculum addresses critical subjects including multi-model AI systems, no-code and low-code AI development, Retrieval-Augmented Generation (RAG), agentic AI, and AI automation via CrawAI. Students will also acquire the skills to construct AI chatbots, AI-enabled knowledge bases, and offline/local AI models, ensuring both privacy and operational efficiency.

This instructor-led, live training in Tashkent (online or onsite) is aimed at backend developers who wish to build MCP-compatible tool servers and integrate them with agent clients.

By the end of this training, participants will be able to implement MCP servers, connect them to Claude Code and OpenClaude, switch transports, and maintain an internal tool registry.

This instructor-led, live training in Tashkent (online or onsite) is aimed at beginner-level / intermediate-level / advanced-level AI professionals who wish to use MCP to connect AI assistants with external tools, data, and enterprise services.

By the end of this training, participants will be able to: explain MCP concepts, identify key architecture components, set up a basic integration, and apply security good practices.

This instructor-led, live training in Tashkent (online or onsite) is aimed at beginner-level to intermediate-level developers and technical teams who wish to use OpenAI APIs to build AI agents that can reason, use tools, and complete multi-step tasks.

By the end of this training, participants will be able to: design agent workflows, connect tools and external data, manage context and responses, and test agents for business use.

This instructor-led live training in Tashkent (online or onsite) is designed for intermediate to advanced-level developers and technical professionals who wish to use OpenClaw to build and run practical AI agents that automate work through chat and tool integrations.

By the end of this training, participants will be able to install and configure OpenClaw, connect an LLM provider, utilize and adapt skills for automation, and apply basic security and operational guardrails.

This instructor-led, live training in Tashkent (online or onsite) is designed for senior developers who wish to use Pi to build lightweight, customizable terminal agent workflows.

By the end of this training, participants will be able to install Pi, extend it with TypeScript skills, adapt workflows to team standards, and share extensions via npm.

This instructor-led, live training in Tashkent (online or on-site) is aimed at developers who wish to use Pi Coding Agent to generate code, review changes, and streamline terminal-based development workflows.

By the end of this training, participants will be able to: explain Pi Coding Agent concepts, set up and use the tool effectively, apply it to practical coding tasks, and adopt safe team workflows.