agentic-engineering-workflow
Agentic Engineering Workflow
This workflow enables you to transition from manual implementation to high-level system architecture by managing autonomous AI agents (like Devin) as "junior buddies." By shifting implementation to agents, you can scale a small team (e.g., 15 engineers) to handle the output of a much larger organization, aiming for 25% to 50% of pull requests to be AI-generated.
Core Principle: Bricklayer to Architect
Most engineering time is spent on "bricklaying": debugging Kubernetes errors, fixing port issues, or writing boilerplate code. Your goal is to move to "architecting": defining the problem precisely, mapping out the solution, and specifying trade-offs, while the agent handles the execution.
1. Task Delegation Framework
Do not hand agents "problems" (ambiguous high-level goals); hand them "tasks" (well-defined, verifiable units of work).
- Verifiability: Choose tasks that have an automated feedback loop (e.g., code that can be run, tests that can pass, or UI that can be previewed).
- The "Junior Buddy" Lens: Treat the agent like a talented but new junior engineer.
- Bad Prompt: "Fix our scaling issues."
- Good Prompt: "I'm seeing a 404 error on the signup page. Research the logs in Datadog, reproduce the bug in a local environment, and suggest a fix."
2. Managing the Asynchronous "Fleet"
Do not watch the AI work action-by-action. To achieve massive productivity gains, you must manage multiple agents in parallel.
- The 5-Devin Rule: Aim to have up to 5 agents running at once.
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