AI Coding Agent for SDLC

Course Details

A 3-day course, 6 hours per day (18 hours in total, 09:00-16:00), delivered as lectures with labs that build on one continuous project across all three days. Intermediate level. Learners must be able to program and bring their own AI coding agent account. The course focuses on the thinking and the workflow across SDLC phases rather than a deep dive into any single tool, so it transfers to other agents as the technology changes. Anyone wanting a Claude Code specific deep dive should take the separate Claude Code Roadmap 12 Units course. Learners take home a lab guide, context templates (AGENTS.md, DESIGN.md) and sample source code from every lab.

Day 1: Understanding Agents, Planning and Context

Section 1: How an AI Coding Agent Works

• Asking an AI one question at a time versus delegating work to an agent that iterates
• The agentic loop: analyze, act, verify and correct until the task is done
• The context window and how an overlong context degrades output quality
• The agent harness and permission model that define what the agent may touch
• Estimating and controlling token cost before it runs away

Section 2: Choosing and Configuring Tooling for the Team

• The categories of AI coding agent in real use and the differences that matter
• Criteria for choosing tooling that fits your technology stack and budget
• Setting permissions so the agent works freely but the codebase stays safe
• Confidentiality considerations when organizational code meets external tools
• Lab: install and configure an agent against your own project

Section 3: Planning with AI

• Using AI to research options and weigh trade-offs before deciding
• Having AI ask questions back to close requirement gaps before work starts
• Breaking requirements into tasks and issues scoped tightly enough for an agent
• Sequencing work and judging which tasks belong to the agent and which to a person
• Lab: break one feature into a task list ready to hand to an agent

Section 4: Preparing Context with AGENTS.md and Conventions

• The role of the context file the agent reads before every piece of work
• Writing AGENTS.md with coding conventions, project structure and everyday commands
• Keeping context tight so the important information is not buried
• Using memory to carry what must persist between sessions
• Sharing one context set across the whole team through Git
• Lab: write the context file for your project and compare output before and after

Section 5: Design System and API Design as Context

• Making the agent follow the existing design system instead of inventing a new style
• Writing DESIGN.md to describe the look, behaviour and existing components
• Fixing the API spec first so frontend and backend can proceed in parallel
• Using the spec as the contract the agent must satisfy and you can verify against
• Lab: write DESIGN.md and the API spec for the feature you planned earlier

Day 2: Building the System and Extending the Agent

Section 6: Frontend Development with an Agent

• Delegating interface work in small rounds rather than asking for a whole page at once
• Making the agent reuse the components and design system you already have
• Reviewing each round and directing precise fixes without rewriting whole files
• Managing state and wiring to the API according to the agreed spec
• Lab: build the interface of a real feature with an agent until it works

Section 7: Backend Development with an Agent

• Having the agent build endpoints against the API spec you wrote
• Designing the data layer and managing schema changes so they stay traceable
• Handling validation, access control and error handling
• Reviewing what the agent wrote before merging it into the main code
• Lab: build the server-side API and connect it to the interface you made

Section 8: Handling Errors, Logs and Shifting Context

• Giving the agent enough context and log detail to actually find the cause
• Recognizing when the agent is looping without progress and how to break the cycle
• Resetting or condensing context when length starts degrading quality
• Using Git as the safety net so you can always roll back
• Lab: work through a real problem in your project alongside the agent

Section 9: Connecting External Tools with MCP

• What the Model Context Protocol adds once the agent can reach external tools
• Configuring an MCP server and connecting it to an organizational database or API
• Restricting each connection on least-privilege principles
• Verifying the agent calls the right tool and gets usable results
• Lab: connect the agent to your project database through MCP

Section 10: Skills, Subagents, Hooks and Plugins

• Building skills so repeatedly explained work becomes instantly callable
• Using subagents to split large work and merge the results
• Using hooks to insert automatic checks at the moments that matter
• Using plugins and distributing one configuration set across the team
• Lab: build a skill and set a hook for your own team standard

Day 3: Quality, Delivery and Operations

Section 11: Agent-Written Tests and Quality Gates

• Deciding what level of test the agent should write and how far coverage should go
• Checking that agent-written tests exercise real behaviour rather than just passing
• Defining the quality gate that must pass before merging to the main branch
• Setting the pipeline to stop itself when quality falls below the bar
• Lab: have the agent write tests for your feature and set up the quality gate

Section 12: Code Review and Security Checks with AI

• Using AI to review code and flag quality and performance risks
• Basic security checks such as user input handling and secret management
• The limits of AI review and where a person must decide
• Writing review comments the code owner can act on
• Lab: review a classmate pull request with AI and summarize what you found

Section 13: End-to-End and QA with Playwright

• Choosing the user journeys worth covering end to end
• Having the agent generate Playwright scripts from test scenarios
• Choosing selectors and handling waits so results stay stable
• Visual regression and producing a testing report the team can read
• Lab: build an end-to-end suite for the main journey and get it passing

Section 14: Deployment, Operations and Automatic Documentation

• Using the agent to prepare and verify the steps before a deployment
• Managing environment variables and secrets safely
• Using agents for routine and scheduled work once the system is live
• Generating and updating project documentation so it keeps pace with the code
• Lab: deploy the feature built over three days along with its documentation

Section 15: Capstone and Team Adoption

• Capstone: walk every SDLC phase on one feature from planning through delivery
• Presenting your workflow and why the agent did or did not handle each phase
• Judging which tooling suits your own team workflow
• How to start with a real team and how to measure whether it pays off
• Workshop: present your work and review the workflow with the instructor