Agentic Software Development: How AI Agent Teams Build Apps

Agentic software development is a way of building software where AI coding agents handle the execution. You describe what you want. The agent figures out how to build it, writes the code, tests it, and iterates until it works. This is different from autocomplete tools that suggest the next few characters. Agentic development means the AI plans and executes entire features.

The term "agentic" matters because it signals a specific capability: autonomy. An agentic system does not wait for instructions at every step. It takes a goal, breaks it into tasks, and works through them on its own. This changes how engineers spend their time, shifting work from writing code to reviewing and directing code.

What is Agentic Development?

Traditional coding with AI looks like this: you type a function signature, the AI suggests the body, you accept or reject, you move on. You are in control. The AI is reactive. It only responds when prompted.

Agentic development flips this. You give the agent a high-level task: "Add a payment system using Stripe with subscription management." The agent reads your codebase, plans the implementation, creates necessary files, writes the integration code, adds error handling, writes tests, and runs them. If tests fail, it reads the error output and fixes the code. It keeps going until the task is done or it gets stuck.

The gap between these two approaches is enormous. Autocomplete saves seconds per line. Agentic development saves hours per feature. But it also requires a different way of thinking about your role as a developer.

How Agentic Development Works

Every agentic coding system follows roughly the same pattern, though the details vary by tool.

Step 1: Task understanding. The agent receives your instruction and analyzes it. Good agents ask clarifying questions if the task is ambiguous. They also read relevant parts of your codebase to understand the current state of the project.

Step 2: Planning. The agent creates an execution plan. This might be explicit (some agents show you the plan and wait for approval) or implicit (the agent just starts working through steps internally). The plan includes which files to modify, what new files to create, and what order to make changes.

Step 3: Execution. The agent writes code, modifies files, installs dependencies, and runs commands. This is where the actual work happens. Good agents make incremental changes rather than rewriting entire files, which makes their work easier to review.

Step 4: Validation. The agent runs tests, linters, or type checkers to verify its work. If something fails, it reads the error and attempts a fix. This loop of execute-test-fix is what gives agents their power. They can iterate much faster than a human switching between editor and terminal.

Step 5: Iteration. If the initial approach does not work, good agents try a different strategy. They might refactor their implementation, change the library they chose, or simplify the approach. Weaker agents just retry the same thing and get stuck in loops.

Single Agent vs Multi-Agent Systems

Most agentic development today uses a single agent. Tools like Claude Code, Cursor Agent, and Windsurf Cascade all use this model. One AI handles everything from planning to execution to testing.

Single Agent

A single agent receives the task and owns it from start to finish. It maintains full context about what it has done, why it made certain choices, and what remains.

Advantages:

• No context loss between steps
• Simpler to debug when things go wrong
• Faster for most tasks since there is no coordination overhead
• Easier to interrupt, redirect, or correct

Disadvantages:

• One agent might not be equally good at all tasks (planning, coding, testing)
• Long tasks can exhaust the agent's context window
• No built-in review step. The same AI that writes the code also validates it

Multi-Agent Systems

Multi-agent systems use specialized agents for different roles. A planner agent decides what to build. A coder agent writes the implementation. A reviewer agent checks the code for bugs and style issues. A tester agent writes and runs tests.

Advantages:

• Each agent can be optimized for its specific role
• Built-in review: the reviewer agent catches mistakes the coder agent made
• Parallel execution. Frontend and backend agents can work at the same time
• Fresh context for each agent. No accumulated confusion from long sessions

Disadvantages:

• Coordination is hard. Agents can produce conflicting code
• Context is lost in handoffs between agents
• More expensive. Each agent call costs tokens
• Debugging is harder when multiple agents contributed to a problem
• Slower for simple tasks where the overhead is not worth it

In practice, the single-agent approach wins for most tasks today. Multi-agent patterns make sense in specific scenarios: large codebases where different parts need simultaneous work, or workflows where an independent review step adds clear value.

Agentic Development in Practice

Here is what a real agentic development session looks like at Devvela when building a feature for a client project.

The task: add a dashboard page that shows usage analytics with charts, filters by date range, and exports to CSV.

We open Claude Code in the project directory. We describe the feature, including which data sources to use, what chart library the project already has, and where the new page should live in the routing structure. The agent reads the existing dashboard layout, the data models, and the API routes. It creates the new page component, adds the API endpoint, writes the data aggregation logic, connects the chart library, adds the date filter, implements CSV export, and writes integration tests.

Total time: about 20 minutes of agent execution, plus 15 minutes of human review and minor adjustments. The manual estimate for the same feature: a full day of development.

But not everything goes this smoothly. The agent sometimes makes assumptions that do not match the client's requirements. It might choose a chart type that does not display the data well. It might implement date filtering in a way that conflicts with how the rest of the app handles timezones. These are the moments where human review matters most.

What works well with agents:

• CRUD features with clear data models
• API integrations where the documentation is public
• Refactoring existing code (renaming, restructuring, splitting files)
• Writing tests for existing code
• Boilerplate and scaffolding
• Bug fixes where the error message is clear

What still needs heavy human involvement:

• Architecture decisions for new systems
• Performance optimization that requires profiling
• Security-sensitive code (auth, payments, data access)
• UI/UX decisions that affect user experience
• Complex business logic with many edge cases

The Role of Human Engineers

Agentic development does not replace software engineers. It changes what they do.

Before agentic tools, engineers spent most of their time typing code. Reading docs, writing implementations, debugging syntax errors, running tests manually. The mechanical work consumed hours.

With agentic development, the mechanical work is handled by the agent. Engineers shift to higher-level work: deciding what to build, how to architect it, reviewing what the agent produced, and catching the mistakes that the AI cannot see.

This requires senior-level thinking. A junior developer might accept whatever the agent generates. A senior developer knows when the agent chose a bad pattern, when the database schema will not scale, when the error handling is too shallow, when the test coverage misses the real failure modes.

The best results come from engineers who understand the full stack deeply and use agents to skip the typing. They spend their time on design, review, and the tricky edge cases that agents consistently miss.

This is why vibe coding agencies like Devvela pair experienced engineers with AI agents. The agents provide speed. The engineers provide judgment.

Getting Started with Agentic Development

If you want to adopt agentic development, here is a practical starting point.

1. Pick one tool and learn it well. Do not try to use five agents at once. Start with one tool that fits your workflow. Claude Code if you like the terminal. Cursor if you prefer an IDE. Use it every day for two weeks.

2. Start with low-risk tasks. Use agents for tests, refactoring, and boilerplate first. These tasks have clear success criteria and are easy to review. Build confidence before using agents for core features.

3. Write good instructions. Agents perform better with specific prompts. Instead of "add authentication," say "add email/password authentication using the existing User model, bcrypt for hashing, JWT for sessions, and add middleware to protect the /api/dashboard routes." The more context you give, the fewer mistakes the agent makes.

4. Always review the output. Read every line the agent writes. Not because it is always wrong, but because the mistakes it does make are often subtle: a missing null check, a race condition, an N+1 query. These are hard for the agent to catch and easy for an experienced developer to spot.

5. Set up guardrails. Use linters, type checkers, and automated tests as a safety net. The agent should run these after every change. If your project does not have tests, write some before introducing agents. Tests are how you verify the agent did not break existing behavior.

For a deeper look at the tools available, see our tools guide in the Cookbook. For specific tool comparisons, check our AI coding agents comparison.