AI Agent Development and Context Engineering Course
Description: After completing the course, participants will be able to demonstrate the following competences:
- Understand and Explain AI Architecture: Articulate the fundamental architecture, capabilities, and limitations of Large Language Models and AI agents, enabling informed decision-making about technology selection and implementation strategies.
- Evaluate and Select AI Solutions: Differentiate between various AI tools, models, and deployment approaches, selecting appropriate solutions for specific business needs based on technical requirements and organizational constraints.
- Master Context Engineering: Design and implement comprehensive context engineering strategies that maximize AI agent effectiveness, the highest-leverage skill in contemporary AI implementation.
- Build AI-Powered Applications: Demonstrate proficiency in building and deploying AI-powered applications using no-code and low-code tools, enabling rapid prototyping and production deployment.
- Create Functional MVPs: Develop complete minimum viable products leveraging AI coding agents and modern development platforms, demonstrating end-to-end product development capability.
- Assess Agent Maturity: Evaluate AI agent maturity levels and assess their readiness for specific business applications, enabling strategic deployment decisions.
- Apply Ethical AI Principles: Integrate ethical considerations and sustainability principles into AI implementation, ensuring responsible deployment aligned with organizational values.
- Demonstrate Tool Proficiency: Exhibit practical competence in using production-grade tools including Claude Desktop, Google AI Studio, Firebase, and coding agents.
- Synthesize Technical and Business Strategy: Integrate technical knowledge with business strategy to identify and capitalize on AI-driven opportunities within organizational contexts.
- Anticipate AI Evolution: Articulate the trajectory of AI development and prepare for emerging capabilities, positioning organizations for continued competitive advantage.
Previous skills/knowledge: Participants are expected to have the following basic knowledge:
- At least three years of professional experience in business, technology, or related fields to provide context for practical application.
- Digital literacy and comfort with contemporary digital tools, cloud-based applications, and web-based platforms.
- English proficiency for effective communication in written and spoken English, as course materials, tools, and instruction are delivered in English.
- No programming experience is required. The program is specifically designed for professionals from diverse backgrounds seeking to leverage AI without traditional software engineering backgrounds.
Authorized Partners:
Teaching requirements:
Trainers should meet the following requirements:
- Subject Matter Expertise: Deep and comprehensive knowledge of Large Language Models, AI agents, Model Context Protocol (MCP), context engineering principles, and practical experience in building and deploying AI-powered applications.
- Technical Proficiency: Demonstrated expertise with production-grade tools including Claude Desktop, Google AI Studio, Google Firebase, coding agents, and contemporary AI development platforms. Experience with both no-code and low-code development approaches.
- Practical Experience: Minimum of 3 years implementing AI solutions in business contexts, including building minimum viable products, deploying web applications, and orchestrating AI agents for complex, multi-step challenges.
Objectives to achieve: This program aims to provide participants with comprehensive practical skills in AI agent development, deployment, and context engineering:
- Master the fundamental distinction between Large Language Models and AI agents, including architectural and functional differences.
- Understand and implement Model Context Protocol, the enabling technology for agent autonomy and tool connectivity.
- Build functional AI agents capable of autonomous execution, tool use, and goal-directed behavior.
- Master context engineering, the discipline of designing comprehensive instruction sets that enable AI agents to tackle complex, multi-step challenges.
- Develop and deploy AI-powered applications using production-grade tools including Claude Desktop, Google AI Studio, and Firebase.
- Create complete minimum viable products using AI coding agents and modern development platforms.
- Evaluate AI agent maturity levels and assess readiness for production deployment in specific business contexts.
- Integrate ethical considerations and sustainability principles into AI implementation.
