What is agentic coding in the Develop phase?

Agentic coding means delegating a scoped task to an AI agent that plans the work, edits across multiple files, runs commands, and returns a diff for you to review. Unlike autocomplete it executes multi-step work; unlike vibe coding, the developer reviews and owns every change.

Does AI write all the code in the Develop phase?

No. Agents can produce a lot of code quickly, but developers stay in control. You define the task and context, review every diff, and own architecture, security, and complex logic. The shift is from writing every line to directing and verifying the work.

Phase 03

Develop AI Coding Agents & Code Generation

Direct coding agents that plan and execute multi-step tasks, generate and refactor code, and hand you reviewable diffs. The job shifts from typing every line to scoping, steering, and reviewing.

What is the Develop Phase?

The Develop phase is where AI does the most visible work - and it has changed the most. AI has moved past autocomplete and inline suggestions to coding agents that take a scoped task, plan it, and execute it across multiple files before handing you a diff to review.

That changes the developer's role rather than removing it. You spend less time typing every line and more time writing clear specs, giving the agent the right context (often through MCP-connected tools), steering it when it drifts, and owning the review of everything it produces. The agent is fast and tireless; the judgment, architecture, and accountability stay with you.

In the Develop phase, AI helps you:

Execute scoped, multi-step tasks as a coding agent
Generate code that follows your project's patterns and style
Refactor existing code for better performance or readability
Debug issues by analyzing error messages and code flow
Explain unfamiliar code or complex algorithms

The discipline that makes this work - clear context and rigorous review - is its own skill. See Context & Spec-Driven Development and the AI Agents & Tools guide.

Quick Reference

AI Role

Coding Agent

Key Output

Working, tested code

Tools

Claude Code, Cursor, Copilot, Codex

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Key Activities

Agentic Task Execution

Hand a coding agent a scoped task and it plans the steps, edits across multiple files, runs commands, and returns a diff - following your project's patterns and style.

Example Prompt: "Add a user data hook with loading, error, and retry states, wire it into the profile page, and add tests. Show me the diff before committing."

Real-Time Review

AI reviews your code as you write, catching bugs, security issues, and suggesting improvements before you commit.

Example Prompt: "Review this function for potential bugs, security vulnerabilities, and performance issues."

Refactoring

AI helps refactor legacy code, extract reusable functions, and improve code structure while maintaining functionality.

Example Prompt: "Refactor this class to use composition instead of inheritance, maintaining the same public interface."

Debugging

AI analyzes error messages, stack traces, and code flow to identify root causes and suggest fixes.

Example Prompt: "This function throws 'undefined is not a function' - analyze the code and explain why this error occurs."

Best Practices

Give Context

Always provide AI with relevant context - your project structure, coding standards, and the specific problem you're solving.

Review Every Agent Diff

An agent can change many files at once. Read, test, and understand the full diff before committing it - you are accountable for code you did not type.

Iterate on Prompts

If the first result isn't right, refine your prompt. Be more specific, provide examples, or break the task into smaller pieces.

Don't Skip Security

AI may generate code with security vulnerabilities. Always run security scans and review for common issues like injection attacks.

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