ODW #4: From Copilot to Pilot, Automating from Implementation to PR with Agentic Coding
LY Corporation’s Orchestration Development Workshop promotes a shift from using AI as a code suggestion tool to using it as an autonomous development “pilot.” The team combined specification-driven development with Jira/Confluence access through MCP so AI agents could investigate requirements, plan implementations, write code, run checks, create pull requests, and respond to reviews. The approach improved development speed and planning, but still requires humans to review AI-generated code and retain responsibility for product quality. ## From Copilot to Agentic Coding - The team initially used GitHub Copilot for small code-generation tasks but saw limited productivity gains. - Two developments enabled a broader shift: - Existing requirements and design documents supported specification-driven development. - Jira and Confluence became accessible to AI coding tools through the Model Context Protocol (MCP). - Agentic coding gives an AI agent a high-level goal, which it decomposes into tasks and executes iteratively. - Unlike autocomplete tools, an agent can analyze the broader codebase, run commands and tests, fix lint errors, and continue until the requested feature is complete. ## Workshop Goals and Human–AI Responsibilities - The workshop focused on automating the process from implementation through pull-request creation. - Humans remained responsible for: - Defining requirements and writing specifications - Final testing, review, and release decisions - AI was assigned: - Implementation planning - Code implementation - Pull-request creation - Initial review and review-response work - This division preserved the existing development process while giving participants practical experience with agentic workflows. ## Stage 1: Research and Implementation Planning - Participants supplied a Jira ticket URL to a custom slash command. - The AI agent: - Retrieved Jira data through the Jira MCP tool - Followed Epic links and collected related tickets - Retrieved Confluence documentation through the Confluence MCP tool - Explored the codebase, using an Explore Agent where available - Wrote a detailed implementation plan to `specs/{ticket-or-topic}/plan.md` - The plan included requirements, affected components, technical analysis, implementation tasks, risks, testing considerations, and a checklist. - Saving the plan to a file made it available for human review, future sessions, and later PR generation. - The team emphasized planning early because vague instructions can lead to incorrect implementations and costly rework. ## Stage 2: Implementation and Pull-Request Creation - The reviewed plan was passed to an implementation command. - The AI was instructed to: - Understand the plan and implementation scope - Modify the code - Add or update tests - Run the test suite - Run linting and build commands - Fix any resulting problems - Explicitly listing these steps encouraged the coding agent to maintain a task checklist and complete the full development cycle. - A separate PR command generated the pull request using the team’s template. - Information gathered during the planning stage could be reused in the PR description, reducing administrative work. ## Stage 3: AI Review and Issue Resolution - An AI screening-review command analyzed the generated PR. - It also read existing comments, including: - The AI’s own prior review comments - Comments from other team members - The agent identified issues requiring changes and explained its assessment of existing comments. - After a human reviewed those conclusions, the AI could implement the necessary fixes, reducing the cost of responding to review feedback. ## Benefits and Risks - **Higher code-generation speed** - Agents can work with less frequent human intervention. - Developers can perform other tasks while agents work. - Multiple agents can potentially run in parallel. - **Earlier risk discovery** - Detailed implementation plans clarify the work before coding begins. - Planning can reveal overlooked tasks, dependencies, and risks. - **Greater review burden** - AI can generate large volumes of code that humans must still inspect. - The unfamiliar workflow may create stress for developers. - **Human accountability remains essential** - Developers are responsible for the quality of AI-generated code. - Poor-quality output increases reviewer workload and can add technical debt. ## Workshop Results - The workshop was delivered twice: - A hands-on practical session requiring prior preparation - An introductory session with more detailed support - Approximately 2,500 people participated. - More than 40% of respondents had already applied, or intended to apply, some aspect of the workshop. - The sessions provided concrete guidance on MCP server usage and effective ways to delegate coding tasks to AI agents. The recommended approach is to introduce agentic coding incrementally: keep human ownership of requirements and final quality decisions, while allowing AI to handle structured planning, implementation, testing, PR creation, and initial review.
Read original(opens in new tab)