How I Structured My Workspace to Work With AI Agents Without Hallucination (Using Proper Context)

After a long time, I’m writing blog. Recently AI agents are becoming part of every developer’s workflow.

Many developers are already using tools like Cursor, Windsurf, Antigravity, Claude Code, and other AI coding assistants to fix bugs, generate code, or automate development tasks.

That’s great.But there is one big mistake I see developers making. They treat AI like this:

Prompt → AI Answer

This approach works sometimes, but when you start using AI agents for real development work, you begin to notice problems:

• AI hallucinating solutions
• inconsistent answers
• wrong assumptions about your project
• repeated instructions in every prompt

The root problem is simple.

The AI doesn’t understand your workspace context.

So instead of writing bigger prompts every time, I decided to structure my workspace specifically for AI agents.

I introduced four key components into my workspace:

• Skills
• Rules
• Workflows
• Browser Subagent

These components give the AI clear instructions, constraints, and capabilities.

Another interesting benefit I discovered is something many developers don’t talk about:

This approach also reduces token usage.

Since the AI already understands the project structure and instructions, you don’t need to repeat long prompts every time. That directly reduces AI cost.

In this blog, I will explain the first and most important concept:

Skills.

Skills: Giving Your AI Agent Specialized Abilities

What Are Skills?

A Skill is a reusable capability you provide to your AI agent.

Think of skills as specialized knowledge modules.

Instead of relying only on its general training, the AI can load specific instructions designed for your project.

A skill typically contains:

• instructions
• task logic
• references or documentation
• optional scripts

According to the Antigravity documentation, skills are modular capabilities that the AI can load when relevant to a task.

Source: https://antigravity.google/docs/skills

This means the AI does not need to remember everything all the time. It simply loads the skill when needed.

This improves both accuracy and efficiency.

Why Skills Are Important for AI Agents

In real projects we have many things that AI cannot automatically guess:

• coding standards
• project architecture
• deployment processes
• commit conventions
• internal tools

Without skills, AI has to infer everything from the prompt, which often leads to hallucinations.

With skills, you encode the knowledge once and let the AI reuse it.

This makes the AI:

• more accurate
• more predictable
• more consistent with your team practices

A Practical Example: Git Commit Skill

Let’s take a simple example.

Many teams follow Conventional Commit messages.

But developers sometimes write commits like this:

fix bug
update code
minor change

These messages are not helpful for maintaining project history.

Instead of correcting them manually, we can create a Git Commit Skill.

Example structure:

skills/
 └── git-commit-formatter
      └── SKILL.md

Example content:

Skill Name: git-commit-formatter

Goal
Generate commit messages using Conventional Commit format.

Instructions
1. Analyze the code changes.
2. Create commit message using:

type(scope): description

Examples
feat(auth): add login endpoint
fix(cart): resolve checkout crash

Now when someone asks the AI:

“Generate a commit message for these changes.”

The AI loads the git-commit-formatter skill and produces the correct format.

No extra explanation required.

How Skills Work Internally

Here is the simplified flow:

1. The user asks the AI to perform a task
2. The AI scans available skills in the workspace
3. It checks the skill descriptions
4. If a skill matches the task → it loads the skill
5. The AI follows the defined instructions

So instead of guessing, the AI follows predefined expertise.

Rules: Guardrails for the AI Agent

Rules define constraints the AI must always follow.

They act like safety policies.

Examples:

• Never expose API keys
• Always use environment variables
• Follow project coding standards
• Do not modify production configuration files

Rules ensure the AI does not generate unsafe or incorrect solutions.

Source: https://antigravity.google/docs/rules-workflows

Example rule:

Never hardcode secrets in code.
Always use environment variables.

Now every AI-generated solution respects this rule.

Workflows: Automating Repeated Development Tasks

Workflows define multi-step automation processes.

Instead of explaining a task step-by-step every time, you define it once.

Example tasks that work well with workflows:

• deployment
• code review
• testing
• release process

Example workflow:

Deployment Workflow

1 Build project
2 Run tests
3 Generate changelog
4 Deploy application

Now the AI can execute the entire workflow automatically.

This removes repetitive instructions from prompts.

Browser Subagent: Giving AI Access to the Web

Another interesting component is the Browser Subagent.

The Browser Subagent allows the AI to interact with web pages inside the Antigravity browser environment.

Source: https://antigravity.google/docs/browser-subagent

This allows the AI to:

• read documentation
• analyze websites
• extract information
• test UI flows

For example:

You can ask:

“Open the API documentation and find the authentication endpoint.”

The browser subagent will:

1. open the webpage
2. analyze the content
3. extract the relevant information
4. return the answer

This makes the AI extremely useful for documentation research and debugging tasks.

My AI Workspace Structure

Here is how I organized my workspace.

.agent
 ├── skills
 │    ├── commit-formatter
 │    ├── db-inspector
 │    └── code-reviewer
 │
 ├── rules
 │    ├── security-rules.md
 │    └── coding-standards.md
 │
 └── workflows
      ├── deploy.md
      └── test.md

This structure helps the AI understand:

• what abilities it has
• what restrictions exist
• what processes it can automate

What I Will Explain in the Next Blog

In the next blog, I will explain something more interesting.

I will show how my Skills, Rules, and Workflows automatically sync with my development workflow, so I don’t have to manage them manually.

This setup allows AI agents to stay aligned with my current development environment without requiring constant updates.

💡 I share short daily insights for developers about AI agents, productivity tools, and modern dev workflows.

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Enjoy your journey with {AI_AGENTS}

Mohammad Azeem

Building **quick commerce software** solution at **Hyperzod.**