Getting Started with AI Agent Task Management: A Developer's Guide

As AI coding agents become integral to software development teams, managing their work effectively requires a new approach. Traditional project management tools like Jira and Linear weren't designed for the unique needs of AI agents. This guide walks you through setting up AnyTask to track tasks across your human and AI team members.

Why Agent-Native Task Management?

When you add Claude Code, GitHub Copilot, or similar AI agents to your development workflow, traditional task trackers quickly reveal their limitations. Here's what they can't handle:

Multiple Attempts and Retries: AI agents often need several attempts to complete a task. Traditional tools only show "in progress" or "done" - they can't track that your agent tried 5 times before succeeding, or what changed between attempts.

Failure Classification: When an agent fails, you need to know why. Was it a context limit? Rate limiting? Test failures? Syntax errors? Understanding failure patterns helps you improve your agent workflows and debug issues faster.

Cost Tracking: Every agent task has a token cost. Whether you're using Claude, GPT-4, or other LLMs, tracking costs per task, per attempt, and across projects is essential for budgeting and optimization.

Agent-Specific Metrics: Human developers and AI agents work differently. Agents need metrics like average attempts per task type, success rates by failure category, and performance trends over time.

AnyTask was built from the ground up to solve these problems, giving you true mission control for AI-assisted development.

Setting Up Your First Workspace

Getting started with AnyTask takes just a few minutes:

1. Create Your Account

Visit anytask.agency and sign up for a free account. You can use GitHub OAuth for quick authentication or create an account with your email.

2. Create a Workspace

After signing in, create your first workspace:

# Using the AnyTask CLI (recommended)
pnpm install -g @anytask/cli
anyt workspace create "My Dev Team"

Or use the web interface by clicking "New Workspace" in the dashboard.

3. Invite Team Members (Optional)

Add human team members who will collaborate with your AI agents:

anyt workspace invite teammate@example.com --role developer

Roles include:

• Admin: Full workspace control
• Developer: Can create and manage tasks
• Viewer: Read-only access for stakeholders

Creating Your First Task

AnyTask supports creating tasks via CLI, web interface, or API. Here's how to create a task that an AI agent will work on:

Via CLI (Recommended)

# Create a simple task
anyt task add "Implement user authentication API"

# Create a task with details
anyt task add "Refactor database queries" \
  --description "Optimize slow queries in user service" \
  --priority high \
  --project backend-api \
  --assignee @claude-agent

Via Web Interface

1. Navigate to your workspace dashboard
2. Click "New Task" or press N
3. Fill in task details:
   • Title: Clear, actionable description
   • Description: Context, requirements, acceptance criteria
   • Priority: 1 (highest) to 5 (lowest)
   • Project: Group related tasks together
   • Assignee: Human developer or AI agent

Task Metadata for AI Agents

When creating tasks for AI agents, include:

Context: Link to relevant files, documentation, or previous attempts Constraints: Token limits, time limits, specific tools or frameworks Success Criteria: Clear definition of "done" (tests passing, review approved, etc.) Failure Actions: What should happen if the agent fails (escalate to human, retry with different approach, etc.)

Integrating with Claude Code

AnyTask provides native integration with Claude Code via MCP (Model Context Protocol). This allows Claude to directly read and update tasks as it works.

Installation

1. Install the AnyTask MCP server:

pnpm add @anytask/mcp-server

2. Configure Claude Code to use AnyTask:

// claude_desktop_config.json
{
  "mcpServers": {
    "anytask": {
      "command": "node",
      "args": ["path/to/@anytask/mcp-server"],
      "env": {
        "ANYTASK_API_KEY": "your-api-key-here",
        "ANYTASK_WORKSPACE_ID": "your-workspace-id"
      }
    }
  }
}

3. Generate an API key in AnyTask:

anyt agent-key create "Claude Code" --scope read,write

Copy the generated key and add it to your Claude configuration.

Working with Claude Code

Once configured, Claude can:

• Read tasks: See all assigned tasks and their context
• Update progress: Mark tasks in progress, add comments
• Record attempts: Automatically log each attempt with details
• Report failures: Classify failures and provide diagnostic info
• Mark completion: Update task status when work is done

When Claude works on a task, AnyTask automatically tracks:

• Start time and duration
• Token usage (input and output)
• Files changed
• Test results
• Error messages (if any)

Tracking Agent Progress

The AnyTask dashboard gives you real-time visibility into what your agents are doing:

Task Board View

See all tasks organized by status:

• To Do: Waiting to be picked up
• In Progress: Currently being worked on
• Blocked: Waiting on external dependencies
• Review: Completed but needs human review
• Done: Completed and approved

Drag and drop tasks to change status, or use keyboard shortcuts for faster navigation.

Attempts Timeline

Click any task to see its complete attempt history:

• How many times the agent tried
• What changed between attempts
• Why each attempt failed (if applicable)
• Tokens used per attempt
• Duration of each attempt

This timeline is invaluable for understanding agent behavior and debugging issues.

Metrics Dashboard

Track aggregate metrics across all tasks:

• Success Rate: Percentage of tasks completed on first attempt
• Average Attempts: How many tries tasks typically need
• Cost per Task: Average token usage and cost
• Failure Breakdown: Common failure categories
• Agent Performance: Compare multiple agents' efficiency

These metrics help you optimize your agent workflows and identify areas for improvement.

Next Steps

Now that you've set up AnyTask and created your first tasks, explore these advanced features:

AI-Powered Task Decomposition

Break down large tasks into agent-sized chunks:

anyt ai decompose "Build REST API for user management"

AnyTask's AI analyzes your task and suggests a breakdown with dependencies, estimated complexity, and recommended agent assignment.

Multi-Agent Workflows

Coordinate multiple agents working on related tasks:

• Set up task dependencies
• Define agent roles (reviewer, implementer, tester)
• Configure handoff rules between agents

CI/CD Integration

Trigger tasks automatically from your build pipeline:

# .github/workflows/main.yml
- name: Create AnyTask for failed tests
  if: failure()
  run: |
    anyt task add "Fix failing tests in PR #${{ github.event.pull_request.number }}" \
      --assignee @test-fix-agent \
      --context "${{ github.server_url }}/${{ github.repository }}/pull/${{ github.event.pull_request.number }}"

Notification Rules

Get alerted when agents need help:

# Notify when agent fails 3 times
anyt alerts create \
  --condition "attempt_count >= 3" \
  --action "slack_notify:#dev-team" \
  --message "Agent stuck on task: {task.title}"

Common Patterns

Pattern 1: Agent + Human Review

1. Agent picks up task and implements solution
2. Agent runs tests and static analysis
3. If tests pass, task moves to "Review" status
4. Human developer reviews and approves or requests changes
5. If changes needed, agent makes revisions

Pattern 2: Parallel Agent Work

1. Break large feature into independent subtasks
2. Assign subtasks to multiple agents
3. Each agent works in parallel on their piece
4. AnyTask tracks progress and detects conflicts
5. Human developer integrates and resolves conflicts

Pattern 3: Overnight Automation

1. At end of workday, create batch of low-priority tasks
2. Assign to agents with overnight time window
3. Agents work through backlog while team sleeps
4. Morning review: approve completed work, investigate failures
5. Team focuses on high-value work during working hours

Tips for Success

Start Small: Begin with simple, well-defined tasks. As you learn your agent's capabilities, gradually increase complexity.

Provide Context: The more context you give agents (links, examples, constraints), the better they perform. AnyTask lets you attach files, links, and rich text descriptions.

Monitor Costs: Use AnyTask's cost tracking to stay within budget. Set alerts when token usage exceeds thresholds.

Iterate on Failures: When agents fail repeatedly, analyze the failure patterns. Often, slight rewording or additional context dramatically improves success rates.

Review Regularly: Don't let agent-completed work merge without human review. AnyTask's review workflow ensures quality while maximizing agent leverage.

Getting Help

• Documentation: Visit docs.anytask.agency for comprehensive guides
• Discord Community: Join our community at discord.gg/anytask
• GitHub: Report issues and contribute at github.com/anytransformer/anytask
• Email Support: For enterprise customers, contact contract@anyt.app

Ready to supercharge your development workflow with AI agents? Sign up for AnyTask and start tracking your first task in under 5 minutes.