- 1-2Days
- Intermediateto Advanced
- 5Hands-on Projects
Course Overview
Master the fundamentals of agent architecture and practical implementation for real-world development workflows. This course takes you from basic agent concepts to building production-ready coding agents that can autonomously write, debug, and improve code across your entire codebase.
Developers, software engineers, and technical professionals who want to build their own development tools and automate coding workflows.
Solid programming experience in at least one language (Python, TypeScript, or similar). Basic understanding of APIs and command-line tools. Familiarity with Git and development workflows.
Topics covered
Comprehensive curriculum covering theoretical foundations and practical implementation
- 01
IDE & AI Integration
Learn how to integrate AI capabilities into modern development environments. Cover code completion, integration patterns, and best practices for IDE plugins and extensions.
- 02
Agent Architectures & Design Patterns
Deep dive into ReAct pattern, Chain-of-Thought reasoning, agent state management, and planning/reflection mechanisms. Understand when and how to apply each pattern.
- 03
Tool Use and Function Calling
Master function schemas, tool selection strategies, error handling, and custom tool creation. Learn how to give your agents powerful capabilities through tool use.
- 04
Code Generation and Analysis
Explore syntax understanding, code parsing, AST manipulation, code quality assessment, and automated documentation generation.
- 05
Debugging and Error Correction
Learn error pattern recognition, automated debugging strategies, test-driven debugging approaches, and fix validation techniques.
- 06
Multi-step Reasoning and Planning
Understand task decomposition, dependency resolution, iterative refinement, and goal achievement strategies for complex multi-step operations.
- 07
Memory and Context Management
Master short-term vs long-term memory systems, context window optimization, retrieval strategies, and knowledge graph integration.
- 08
Testing and Evaluation Frameworks
Learn agent performance metrics, test suite generation, benchmark creation, and continuous evaluation methodologies.
- 09
Integration with Development Workflows
Explore CI/CD integration, Git workflows, code review automation, and deployment strategies for production environments.
- 10
Real-world Agent Projects and Case Studies
Study production agent architectures, scaling strategies, common pitfalls, and proven solutions from real-world implementations.
Learning Outcomes
By the end of this course, you will be able to:
- Build fully functional coding agents from scratch using modern AI frameworks
- Implement advanced tool-using capabilities with proper error handling
- Design complex agent workflows for multi-step coding tasks
- Evaluate and systematically improve agent performance with metrics
- Deploy agents in production environments safely and reliably
- Debug and optimize agent behavior for real-world use cases
- Create custom tools and functions for specific development needs
Build real-world coding agents through progressive, practical projects
- 01Project 1: Basic Coding Agent with Tool UseBuild a foundational coding agent that can read files, write code, and execute commands. Learn the basics of tool use and agent-environment interaction.
- 02Project 2: Multi-file Refactoring AgentCreate an agent that can analyze and refactor code across multiple files, understanding dependencies and maintaining code quality.
- 03Project 3: Debugging Assistant AgentBuild an intelligent debugging agent that can identify errors, suggest fixes, and validate solutions through automated testing.
- 04Project 4: Code Review Automation AgentDevelop an agent that performs comprehensive code reviews, checking for best practices, security issues, and suggesting improvements.
- 05Final project: production-ready coding agentBring it all together to build a production-ready coding agent tailored to your tech stack, with proper error handling, testing, and deployment strategies.
What people ask before they enrol.
Solid programming in one language, Python or TypeScript or similar, and a working understanding of APIs. You do not need prior agent experience; that is what the course is for.
Building. Using an assistant well is a different and shorter conversation. This is agent architecture: how to give a model tools, how to keep it inside a boundary, and how to tell when it has gone wrong.
The architecture will. Prompts and model names age quickly, so the course spends its time on the parts that do not: control flow, tool design, evaluation and the failure modes.
Yes, and that is the version that sticks. The public course uses neutral examples; run privately, the projects are your repositories and your standards.
Bring us the problem nobody has cracked yet.
We are a small team of senior specialists. We pick the right model and the right layer, and we build the least machinery that does the job. You get a call with an engineer, not a sales deck.