How to Assign Tasks to AI Coding Agents: A Practical Guide

Stop writing code. Start assigning tasks.

That single shift in thinking separates developers who spend hours debugging from those who ship features before lunch. AI coding agents have evolved beyond autocomplete and code suggestions. They can now take a task description, understand your codebase, write the implementation, run tests, and open a pull request—all while you focus on architecture decisions or grab another coffee.

This guide walks you through exactly how to assign tasks to AI agents effectively. You'll learn the different methods available, how to write prompts that get results, and the workflow that turns AI from a novelty into your most reliable team member.

The Mindset Shift: Think Like a Tech Lead

The biggest mistake developers make with AI agents is treating them like fancy autocomplete. They type "write a function that validates email" and wonder why the output doesn't fit their codebase.

Here's the shift: stop thinking like a coder and start thinking like a tech lead.

From "write this function" to "implement this feature."

A tech lead doesn't dictate every line of code. They describe outcomes, provide context, and trust their team to figure out the implementation details. Your AI agent works the same way.

Instead of: "Write a function called validateEmail that uses regex to check if a string is a valid email format"

Try: "Add email validation to the user registration form. Invalid emails should show an error message below the input field. Use our existing form validation patterns in /components/forms."

The second prompt describes what you want to achieve, not how to achieve it. It gives the agent room to make implementation decisions while staying consistent with your codebase.

Describe outcomes, not implementations. Tell the agent what success looks like. Let it figure out the path to get there.

Five Ways to Assign Tasks to Blackbox AI Agents

Blackbox gives you multiple channels for AI task assignment, so you can delegate work however fits your workflow.

1. CLI: The /remote Command

The fastest method for developers already in their terminal. Use the /remote command to assign tasks directly from the Blackbox CLI:

2. API: POST /tasks Endpoint

For programmatic task assignment, integrate with the REST API. Perfect for CI/CD pipelines, Slack bots, or custom tooling:

3. Voice: Call the AI

Yes, you can literally call your AI agent. Dial +1 (940) 290-8999 and describe your task. The agent transcribes your request, processes it, and gets to work. Ideal for assigning tasks while commuting or when typing isn't convenient.

4. SMS: Text Your Task

Send a text message with your task description to the same number. Quick, simple, and works from any phone.

5. Web: cloud.blackbox.ai

The web interface at cloud.blackbox.ai provides a visual dashboard for task management. Create tasks, monitor progress, review outputs, and manage your repositories all in one place.

Writing Effective Task Prompts

The quality of your AI task assignment directly determines the quality of the output. Here's how to write prompts that work.

Be Specific About the Outcome

Vague prompts produce vague results. Define exactly what "done" looks like.

Bad: "Improve the login page"

Good: "Add a 'Remember Me' checkbox to the login page that persists the user session for 30 days when checked. The checkbox should appear below the password field and above the submit button."

Include Context

Your agent needs to understand where it's working. Specify the repository, branch, and relevant files.

Bad: "Fix the authentication bug"

Good: "Fix the authentication bug in the /auth/login.ts file where users with special characters in their passwords can't log in. The issue is in the password sanitization function."

Mention Constraints

Tell the agent what not to break. This prevents well-intentioned changes from causing regressions.

Bad: "Refactor the database queries"

Good: "Refactor the database queries in /services/userService.ts to use parameterized queries. Don't modify the function signatures—other modules depend on them. Ensure all existing tests still pass."

Good vs. Bad Prompts: A Comparison

Task Examples by Type

Different task types benefit from different prompt structures. Here are templates for common scenarios.

Feature Implementation

Bug Fix

Refactor

Test Coverage

Documentation

CLI Workflow Tutorial

Let's walk through a complete AI coding workflow using the CLI.

Step 1: Assign the task

Step 2: Monitor progress

The agent begins working immediately. Check status anytime:

You'll see real-time updates as the agent analyzes your codebase, plans the implementation, writes code, and runs tests.

Step 3: Review the output

When complete, the agent creates a pull request with all changes. Review the diff, check the test results, and either merge or request modifications.

Checking Task Status

Stay informed without micromanaging. Use these commands to monitor your AI agent:

• /status — View current task progress

• /tasks — List all active and recent tasks

• /logs [task-id] — See detailed execution logs

The web dashboard at cloud.blackbox.ai also provides real-time status updates with visual progress indicators.

Reviewing Agent Output

Every completed task results in a pull request. This keeps you in control while letting the agent do the heavy lifting.

Review the diff: Examine exactly what changed. The agent includes commit messages explaining each modification.

Check test results: The agent runs your test suite automatically. Review the results before merging.

Merge or request changes: Approve and merge if everything looks good. If something needs adjustment, add comments to the PR or assign a follow-up task with specific feedback.

Tips for Best Results

After thousands of tasks, these patterns consistently produce better outcomes.

Start with small tasks. Build trust incrementally. Assign a simple bug fix before asking for a major feature. You'll learn how the agent interprets your prompts and can adjust accordingly.

Be explicit about file paths. Don't make the agent guess. Specify exactly which files to modify, especially in large codebases with similar naming conventions.

Include acceptance criteria. Define what success looks like. "The user should see a success toast after saving" is more useful than "make sure it works."

Reference existing patterns. Point to examples in your codebase. "Follow the pattern used in /components/UserCard.tsx" gives the agent a concrete template.

Specify what not to change. Constraints prevent scope creep. "Don't modify the API response format" keeps changes focused.

Start Assigning Tasks Today

You now have everything you need to integrate AI agents into your coding workflow. The developers shipping fastest aren't writing more code—they're writing better task descriptions and letting AI handle the implementation.

Pick one small task from your backlog right now. Open your terminal, type /remote, and describe what you want. Watch the agent work. Review the pull request. Merge it.

That's your new workflow. Welcome to agentic development.

Ready to assign your first task? Install the Blackbox CLI and run /remote with your repository. Your AI agent is standing by.