Agentic AI: when AI scaffolds whole features for you

Agentic AI goes beyond autocomplete. Instead of suggesting the next line, an agent can receive a high-level goal (e.g., "add a checkout flow" or "migrate this to Server Components") and break it into steps, write code, run commands, and iterate until done. Developers act as architects, reviewing and directing rather than typing every line. This post explains how agentic workflows fit into modern development.

What is agentic AI?

Agentic AI refers to AI systems that can:

• Plan: Break a high-level goal into steps
• Execute: Write code, run commands, edit files
• Use tools: Read your codebase, run tests, check linters
• Iterate: Use feedback (errors, test failures) to correct themselves
• Continue: Work across multiple turns until the task is complete

This is different from a single-turn assistant that answers one question. Agentic AI acts more like a junior developer: you give it a task, it tries to complete it, and you review the result.

Examples of agentic AI tools for developers include Cursor Composer, GitHub Copilot Workspace, Lovable, v0, and various AI-powered code generation platforms.

How does agentic AI change the workflow?

With agentic AI, your role shifts from writing every line to specifying intent and reviewing output:

Traditional workflow

1. You understand the requirement
2. You design the solution
3. You write the code line by line
4. You test and debug
5. You refine and merge

Agentic workflow

1. You understand the requirement
2. You describe the goal to the agent (with constraints)
3. The agent plans and implements
4. You review the diff, adjust as needed
5. You test and merge

For well-scoped tasks, this can be significantly faster than writing from scratch. For ambiguous or complex tasks, you may need to break them down, iterate with the agent, or take over manually.

What can agentic AI do?

Here are concrete examples of what agentic AI can accomplish:

Each of these would take multiple steps and potentially multiple files. Agentic AI handles the coordination.

When does agentic AI help most?

Agentic workflows shine for:

• Boilerplate and scaffolding: Creating new components, routes, or modules with standard patterns
• Repetitive refactors: Renaming, restructuring, or converting many files the same way
• Adding tests: Generating test outlines for existing code
• Framework migrations: Moving between versions or patterns (e.g., class to functional, REST to tRPC)
• Implementing specs: Building features from clear requirements or designs

They work less well when:

• Requirements are vague: The agent cannot read your mind; unclear goals produce unclear code
• The codebase is unfamiliar: Agents may not understand complex dependencies or conventions
• The change is critical: Security, authentication, or payment code needs human judgment
• Many systems are interconnected: Large-scale architecture changes require context the agent may not have

Use agents for bounded tasks; use your judgment for architecture and critical paths.

How to give good instructions to an agent

The quality of agent output depends on your instructions. Here are tips:

1. Be specific: "Add a checkout flow" is vague. "Add a checkout page at /checkout with a form for shipping address and a button that calls the createOrder API" is actionable.
2. Specify constraints: Language, framework, patterns, and dependencies. "Use React Hook Form for validation, Tailwind for styling, no external date libraries."
3. Provide context: Reference existing files, types, or patterns. "Follow the pattern in /components/UserProfile for the new component."
4. Break down large tasks: If the task is complex, split it into smaller pieces the agent can handle one at a time.
5. Review and iterate: If the first output is not right, provide feedback. "The styling is off; use the card pattern from the design system."

Clear instructions lead to better first drafts and less rework.

How to keep agent output maintainable

Agents can produce verbose, inconsistent, or non-idiomatic code. Here are practices that keep quality high:

• Enforce style with linters and formatters: Use Biome, ESLint, Prettier, or your team's tooling. Run them on every agent output.
• Prefer small, reviewable changes: Ask for one feature at a time rather than a massive rewrite. Smaller diffs are easier to review and less risky.
• Run tests and type checks: After each agent pass, run your test suite and type checker. Fix failures before continuing.
• Review like any other code: Read the diff carefully. Look for wrong APIs, missing edge cases, security issues, and convention violations.
• Simplify and refactor: Agents often produce more code than necessary. Remove dead code, simplify logic, and align with your patterns.

Treat agent output as a first draft: useful, but not production-ready without your review.

Tools for agentic development

Here are some of the leading agentic AI tools for developers:

For a deeper look at Cursor's agent features, see What Cursor Composer 1.5 means for developers. For general AI coding tips, see AI-assisted coding: practical tips.

Summary

Agentic AI is changing how developers work. Instead of writing every line, you specify intent and review output. This can dramatically speed up well-scoped tasks like scaffolding, refactoring, and test generation.

To use agents effectively:

1. Give clear, specific instructions with constraints and context
2. Break large tasks into smaller, reviewable pieces
3. Run linters, formatters, and tests on every output
4. Review diffs like any other code
5. Use your judgment for architecture, security, and critical paths

Agentic AI is a powerful tool, not a replacement for developer expertise. Use it to accelerate the mechanical parts of coding, and keep your hands on the wheel for everything else.