When AI coding agents like Claude Code, GitHub Copilot, or Devin join your development team, your project management tools need to evolve. Traditional task trackers were designed for humans - they assume tasks are attempted once and either succeed or fail. AI agents behave differently, and managing them effectively requires new capabilities.
Here are five critical features you can't live without when managing AI coding agents.
1. Attempt & Retry Tracking
The Problem: Traditional tools show a binary state - "in progress" or "done". When an AI agent tries a task three times before succeeding, you lose all that valuable information.
Why It Matters: Understanding attempt patterns helps you optimize agent workflows. If your agent consistently needs 4-5 tries on certain task types, that's actionable intelligence. Maybe those tasks need better context, or maybe they're too complex and should be decomposed.
What Good Tracking Looks Like:
Every attempt should capture:
- Timestamp: When did the agent start this attempt?
- Duration: How long did it take?
- Token Usage: Input and output tokens consumed
- Result: Success, failure, or partial completion
- Changes Made: Which files were modified
- Test Results: Which tests passed or failed
- Error Context: Stack traces, error messages, logs
Example Scenario:
Your agent attempts to implement a new API endpoint:
Attempt 1 (2 min, 15k tokens): Failed - missing imports Attempt 2 (3 min, 22k tokens): Failed - tests fail (authentication error) Attempt 3 (4 min, 28k tokens): Success - all tests pass
With this data, you can:
- See the agent learned from previous attempts
- Calculate the true cost (65k tokens total)
- Understand token usage grew as the agent added more context
- Identify patterns (authentication often requires multiple attempts)
AnyTask Implementation:
# View attempt history for any task anyt task attempts show task-123 # Export attempt data for analysis anyt analytics export --format json --filter "attempts > 2"
2. Failure Classification
The Problem: When an agent fails, "error" or "failed" isn't enough information. You need to know why it failed to take appropriate action.
Why It Matters: Different failure types require different responses:
- Context Limit Exceeded: Break task into smaller pieces
- Rate Limited: Retry with backoff
- Test Failures: Agent can often self-correct with test output
- Syntax Errors: Usually fixable on retry
- Conceptual Errors: May need human intervention
Failure Taxonomy:
AnyTask classifies failures into actionable categories:
Technical Failures:
context_limit: Agent hit token context windowrate_limit: API rate limiting kicked intimeout: Task took longer than allowedparse_error: Generated code has syntax errorstest_failure: Tests failed after implementation
Logical Failures:
incomplete: Agent stopped before finishingwrong_approach: Solution doesn't match requirementsscope_creep: Agent tried to do more than askeddependency_missing: Required files or services unavailable
External Failures:
service_unavailable: External API or service downauth_failure: Authentication or permissions issueresource_exhausted: Out of memory, disk, etc.
Example Use Case:
Set up automatic responses based on failure type:
# Retry with more context if context limit hit anyt rule create \ --trigger "failure_type == context_limit" \ --action "retry_with_context" \ --max-retries 2 # Alert humans for logical failures anyt rule create \ --trigger "failure_type in [wrong_approach, scope_creep]" \ --action "assign_human_reviewer"
Analytics Benefits:
Track failure patterns across your team:
Top Failure Categories (Last 30 Days): 1. test_failure (42%) - Usually self-correcting 2. context_limit (23%) - Consider task decomposition 3. rate_limit (15%) - May need higher tier API access 4. wrong_approach (12%) - Needs better task descriptions 5. timeout (8%) - Tasks may be too complex
3. Cost & Performance Metrics
The Problem: AI agents aren't free. Every task consumes tokens, and costs can add up quickly if you're not tracking them.
Why It Matters: Without cost visibility:
- You can't budget for agent usage
- Inefficient workflows go unnoticed
- You don't know which task types are expensive
- ROI calculations are impossible
Key Metrics to Track:
Per-Task Metrics:
- Total token usage (input + output)
- Estimated cost (based on your LLM pricing)
- Number of attempts needed
- Time to completion
- Cost per successful completion
Per-Agent Metrics:
- Average cost per task
- Success rate on first attempt
- Total monthly spending
- Most expensive task types
- Cost trend over time
Per-Project Metrics:
- Total project AI cost
- Cost breakdown by task type
- ROI compared to human developer time
- Budget vs actual spending
Example Dashboard:
Project: Backend API Refactor ───────────────────────────────────────── Total Tasks: 47 Completed: 42 (89%) Total Cost: $127.43 Cost Breakdown: Implementation: $89.20 (70%) Testing: $23.15 (18%) Documentation: $15.08 (12%) Average Cost/Task: $3.03 Most Expensive: "Migrate auth system" ($18.50) Trend: -12% vs last sprint
Budget Alerts:
Set spending limits and get notified before overspending:
# Alert when project exceeds budget anyt budget set \ --project backend-api \ --monthly-limit 500 \ --alert-threshold 80 # Alert on expensive tasks anyt budget alert \ --condition "task_cost > 20" \ --action "notify_slack:#eng-leads"
Cost Optimization:
Use cost data to optimize:
- Switch models: Use cheaper models for simple tasks
- Batch operations: Group similar tasks to reduce context overhead
- Cache results: Reuse agent outputs when appropriate
- Task decomposition: Break expensive tasks into cheaper subtasks
4. Real-Time Collaboration Between Agents and Humans
The Problem: In traditional tools, you can't easily see what agents are doing in real-time, or interrupt them when needed.
Why It Matters: Effective human-AI collaboration requires:
- Visibility: Know what agents are working on right now
- Control: Ability to pause, redirect, or take over if needed
- Communication: Bidirectional updates between humans and agents
- Conflict Prevention: Avoid agents and humans editing the same files
Real-Time Features:
Live Status Updates:
🤖 @claude-agent Currently: Implementing user authentication Progress: 60% (2/3 tests passing) Tokens Used: 45k / 100k limit Next: Adding password reset flow 👤 @sarah-dev Currently: Reviewing PR #342 Blocked on: Agent tests in PR #339
Collaborative Editing:
- See which files agents are modifying
- Lock files to prevent conflicts
- Get notified when agent finishes so you can review
- Leave comments agents can read on next attempt
Task Handoff:
# Agent hands task to human after 3 failed attempts anyt task assign task-456 --to @human-dev \ --note "Hit context limit - needs architecture decision" # Human clarifies and hands back to agent anyt task assign task-456 --to @claude-agent \ --context "Use Redis for caching, not in-memory"
Conflict Detection:
Prevent wasted work when humans and agents edit the same code:
⚠️ Conflict Detected File: src/auth/login.ts Agent @claude-agent: Modified 2 min ago Human @sarah-dev: Opened in editor now Recommendation: Sarah should review agent changes before editing
Synchronous Mode:
For critical tasks, work alongside agents in real-time:
# Start pairing session with agent anyt pair start --agent claude --task task-789 # Agent shows its reasoning as it works # You can provide feedback mid-task # Agent incorporates your input immediately
5. CLI-First Workflow
The Problem: Developers live in the terminal. Context-switching to a web UI breaks flow and reduces productivity.
Why It Matters:
- Speed: CLI operations are faster than clicking through UIs
- Scriptability: Automate common workflows
- Integration: Easy to integrate with git, CI/CD, and other dev tools
- Terminal AI: Many AI agents work in the terminal (Claude Code, Copilot CLI)
Essential CLI Capabilities:
Task Management:
# View tasks anyt list # All tasks anyt list --status in_progress # Filtered anyt list --assignee @claude # By agent # Create tasks anyt add "Fix login bug" anyt add "Refactor API" --description "$(cat context.md)" # Update tasks anyt status task-123 done anyt assign task-456 @sarah-dev anyt comment task-789 "Great work, approved!"
Agent Operations:
# Manage agent keys anyt agent-key create "Claude Dev" --scope read,write,execute anyt agent-key list anyt agent-key revoke key-abc123 # Monitor agent activity anyt agent watch @claude-agent # Live stream of agent activity anyt agent stats @claude-agent # Performance metrics
Analytics & Reporting:
# Quick stats anyt stats today anyt stats --project backend-api --range 30days # Detailed reports anyt report cost --format table anyt report failures --group-by category anyt report agents --sort-by success_rate
Integration with Git:
# Create tasks from commits git log --oneline -5 | anyt import --as-tasks # Link tasks to branches anyt link task-123 --branch feature/auth-fix # Auto-update tasks on commit git commit -m "Fix auth bug [anyt:task-123]" # Task automatically marked as in review
CI/CD Integration:
# .github/workflows/ci.yml
- name: Report test results to AnyTask
if: always()
run: |
anyt task update $TASK_ID \
--test-results ./test-output.xml \
--status $(if [ $? -eq 0 ]; then echo "passed"; else echo "failed"; fi)
Offline Support:
# CLI works offline, syncs when connection available anyt add "Fix bug" --offline anyt list --offline # Shows cached data # Manual sync when ready anyt sync
Customization:
# Aliases for common workflows anyt alias add "todo" "list --status todo --assignee me" anyt alias add "sprint" "list --project current-sprint --board" # Now use shortcuts anyt todo # Your personal todo list anyt sprint # Current sprint board
Putting It All Together
These five features work together to create an effective agent management workflow:
Morning Standup:
# Check what agents did overnight anyt agents activity --since midnight # Review completed tasks anyt list --status done --since yesterday # Check for failures needing attention anyt list --status failed --attempts ">= 3"
During the Day:
# Create tasks for agents anyt add "Write API docs" --assignee @claude # Monitor in real-time anyt watch --agents all # Step in when needed anyt pause task-789 anyt assign task-789 @me --note "Taking over - needs human judgment"
End of Day:
# Review costs anyt cost today # Check tomorrow's queue anyt list --status todo --priority high # Set up overnight batch anyt batch create --tasks 10 --priority low --agent @claude-nightly
Conclusion
Managing AI coding agents effectively requires purpose-built tools. These five features - attempt tracking, failure classification, cost metrics, real-time collaboration, and CLI-first workflow - aren't nice-to-haves. They're essential for getting ROI from your agent investments.
Traditional project management tools will eventually add some of these capabilities, but they're retrofitting features onto architecture designed for humans. Agent-native tools like AnyTask are built from the ground up with these use cases in mind.
Ready to manage your AI agents effectively? Try AnyTask free and see the difference agent-native tooling makes.