Can AI agents really accelerate software development?

Can AI agents really accelerate meaningful software development without compromising code quality?

The answer is Yes. At Legitt, we have built AI-native product, so it was natural for us to ask ourselves this simple question.  Over the past several months, we have arrived at a model that works well for us. This article explains, how we use coding agents at Legitt, what problems they solve, and equally important – how we keep humans firmly in control.

Modern software  development is no longer limited by tools. It is limited by attention, context switching, and the sheer volume of code that needs to be written, reviewed, and maintained.

What We Mean by Coding Agents?

When we say – coding agents – we are not referring to simple autocomplete tools or IDE copilots. At Legitt, a coding agent is a task-driven system which can:

• Understand a feature requirement
• Generate working code across multiple files
• Iterate on feedback
• Improve existing code
• Collaborate with humans and other agents

These agents are not autonomous decision-makers. They operate inside well-defined boundaries and are always supervised by engineers.  Keeping in view the architecture that we have, our core top most priority remains security of code base.

Our Core Principle: Isolation Before Integration

One of the biggest risks with AI-assisted development is letting generated code directly affect production repositories. We avoid this entirely.

Separate Agent Repositories

For every major codebase, we maintain separate repositories dedicated to AI agents.

• These repositories are clones of the actual production repositories
• They follow the same structure, conventions, and dependencies
• No agent ever writes directly to the main product repository

This isolation gives us safety, clarity, and confidence. Agents experiment freely. Production code stays protected.

How the Workflow Actually Works

Feature Definition by Humans: Every task starts with a human-defined goal:

1. A new product feature
2. A backend capability
3. A UI component
4. An integration or refactor

Product engineers provide:

• High-level requirements
• Constraints
• Expected outcomes

This context is critical. Agents perform best when the problem is clearly framed.

Base Feature Development by the Agent: The coding agent then works entirely inside its own repository.

Typical responsibilities:

• Creating initial project scaffolding
• Writing core business logic
• Implementing APIs or UI components
• Handling common edge cases
• Generating basic tests

At this stage, the goal is functionality, not perfection.

The agent is optimized for:

• Speed
• Coverage
• Structural correctness

Human Review of the Foundation: Once the basics are working,

• A product engineer reviews the agent’s implementation
• Architectural decisions are validated
• Security, performance, and scalability are evaluated
• Gaps or improvements are identified

No code moves forward without this step.

Refinement Using Another Agent (Human-in-the-Loop): Here’s where things get interesting. Instead of manually rewriting everything, the engineer:

• Uses another agent to refine, optimize, or clean up the code
• Adds stricter constraints and feedback
• Focuses the agent on specific improvements

Examples:

• Refactoring for readability
• Improving performance
• Enhancing error handling
• Aligning code with internal standards

This creates a collaborative loop:

>> Agent builds >> Human reviews >> Another agent refines >> Human approves

Merge Into the Main Repository: Only after:

• Functional validation
• Code quality checks
• Human approval

The changes are merged into the actual product repository using standard pull request workflows. From Git’s perspective, nothing special is happening. From an engineering productivity perspective, everything has changed.

Why This Model Works for Us

Clear Ownership

• Engineers remain responsible for architecture and decisions
• Agents do not “own” any code
• Accountability never shifts away from humans

Reduced Cognitive Load:

Agents handle:

• Boilerplate
• Repetitive patterns
• Initial drafts

Engineers focus on:

• Product thinking
• System design
• Edge cases
• Long-term maintainability

Faster Iteration Without Risk: Because agents work in isolated repositories:

• Experiments are cheap
• Mistakes are safe
• Iteration is fast

What Coding Agents Are Not Used For: We are deliberate about where we draw the line. We do not use agents to:

• Make architectural decisions without review
• Push code directly to production
• Bypass security or compliance checks
• Replace engineering judgment

Coding agents are accelerators—not substitutes for experience.

Try it now

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Lessons We have Learned

1. Context is everything:  Poor inputs produce poor outputs. Clear requirements matter more than clever prompts.

2.Human review is non-negotiable: The quality jump happens during review, not generation.

3. Multiple agents > one agent: One agent to build, another to refine works better than a single pass.

4. Isolation enables trust: Separate repositories remove fear and resistance from teams.

Looking Ahead

We see coding agents becoming a permanent part of how software is built at Legitt. Next, we are exploring:

• Multi-agent collaboration for larger features
• Agent-assisted code reviews
• Automated test expansion
• Safer integration into CI/CD workflows

But our philosophy will remain unchanged: AI should amplify engineers -not replace them. At Legitt, coding agents help us move faster, reduce friction, and stay focused on building meaningful products. By combining agent-driven development with strong human oversight, we have found a balance that delivers speed without sacrificing quality.

This approach is still evolving – but it is already reshaping how we build Legitt.

Reach out to us if you have any questions. ravi.baranwal@legittai.com.