Best Practices

Writing effective tasks

Start with clear objectives

Define success criteria upfront:

Calculate 15% commission on $50,000. Expected result: $7,500

Use progressive complexity

Start simple, then increase complexity:

Week 1: Simple tasks

Basic calculations, single-step operations

Week 2: Medium tasks

Research, multi-step workflows

Week 3: Complex tasks

Multiple tools, external integrations

Week 4: Production use

Full automation, batch processing

Checkpoint strategies

When to use auto-detect

Use auto-detect when:

You’re new to the system
Task involves external actions (emails, purchases)
You don’t know optimal verification points
Task complexity is high

Example:

{
  "autoDetectCheckpoints": true,
  "testMode": "with-verification"
}

When to use manual checkpoints

Use manual checkpoints when:

Testing specific decision points
You know exactly where verification is needed
Task has well-defined stages
Regulatory requirements dictate specific checks

Example:

{
  "checkpoints": [
    {"stage": "calculation", "description": "Verify math"},
    {"stage": "submission", "description": "Approve sending"}
  ],
  "testMode": "with-verification"
}

Optimal checkpoint placement

2-4 checkpoints is ideal for most tasks

Too few (0-1): May miss critical verification points
Just right (2-4): Balance oversight with efficiency
Too many (5+): Slows down evaluation, verification fatigue

Verification workflow tips

Responding to checkpoints

approve

Agent continues to next step

reject

Agent stops execution

hint: [text]

Agent adjusts approach with your guidance

Using hints effectively

When to use hints

Agent is close but needs adjustment
You want to guide without rejecting
Teaching agent better approaches
Minor corrections needed

Good hint examples

hint: Use CoinMarketCap instead of CoinGecko for more accurate prices
hint: Include all team members in the CC field
hint: Round to 2 decimal places
hint: Use the Alternative Simplified Credit method, not regular method

Bad hint examples

hint: Do it better
hint: Wrong
hint: Not that

Be specific about what to change!

Testing strategies

Baseline comparisons

Always run tasks both ways to measure human impact:

# Run 1: With verification
{
  "taskName": "Task A - With Human",
  "testMode": "with-verification",
  "autoDetectCheckpoints": true
}

# Run 2: Without verification (baseline)
{
  "taskName": "Task A - Baseline",
  "testMode": "without-verification"
}

Metrics to compare:

Success rate
Execution time
Cost per task
Error rate
Quality scores

A/B testing checkpoints

Test different checkpoint strategies:

# Version A: Auto-detect
{
  "taskName": "Test - Auto Checkpoints",
  "autoDetectCheckpoints": true
}

# Version B: Manual
{
  "taskName": "Test - Manual Checkpoints",
  "checkpoints": [/* specific points */]
}

# Version C: No checkpoints
{
  "taskName": "Test - No Checkpoints",
  "testMode": "without-verification"
}

Compare which approach gives best results for your use case.

Performance optimization

Batch processing

Submit multiple tasks efficiently:

import concurrent.futures
import requests

def submit_task(task_data):
    response = requests.post(
        "https://app.mixus.ai/api/eval/create-task-agent",
        headers={"Authorization": f"Bearer {API_KEY}"},
        json=task_data
    )
    return response.json()

# Submit 10 tasks in parallel
tasks = [create_task_data(i) for i in range(10)]
with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:
    results = list(executor.map(submit_task, tasks))

Rate limits: Max 10 concurrent evaluations per organization

Webhook vs polling

Use Webhooks

When: Long-running tasks (>5 min)Benefits:

No polling overhead
Real-time notifications
Lower API usage

Use Polling

When: Short tasks (<5 min)Benefits:

Simpler setup
No server needed
Direct control

Optimizing task descriptions

Shorter is fasterAgent processing time increases with description length. Be concise but clear.

Research all available information about the top three artificial intelligence 
agent platforms currently available in the market, including detailed pricing 
information for all tiers and plans, comprehensive lists of features and 
capabilities, target market analysis, and competitive positioning...

Cost optimization

Understanding costs

Evaluation costs include:

AI model usage - Based on tokens processed
Tool usage - External API calls (web search, integrations)
Verification overhead - Human review time (no additional cost)

Reducing costs

1. Use baseline mode for simple tasks

Skip verification for low-risk tasks:

{"testMode": "without-verification"}

Saves verification overhead, completes faster.

2. Optimize checkpoint count

Fewer checkpoints = lower cost:

Auto-detect typically finds 1-3 checkpoints
Manual: Only checkpoint truly critical steps
Baseline: No checkpoints = lowest cost

3. Batch similar tasks

Group related tasks to reuse context:

[
  {"taskName": "Calc 1", "description": "15% of $50k"},
  {"taskName": "Calc 2", "description": "15% of $75k"},
  {"taskName": "Calc 3", "description": "15% of $100k"}
]

4. Use precise task descriptions

Reduce unnecessary research and tool calls:

// More expensive
{"description": "Find crypto prices and calculate"}

// Less expensive
{"description": "Calculate: 0.5 BTC × $42,000 + 10 ETH × $2,200"}

Quality assurance

Validation strategies

Define expected outcomes

Specify what “correct” looks like before running

Run with verification first

Verify agent behavior manually before automation

Compare with baseline

Measure improvement from human oversight

Iterate on prompts

Refine task descriptions based on results

Tracking quality metrics

Monitor these metrics over time:

metrics = {
    "success_rate": 0.95,      # % of successful completions
    "approval_rate": 0.92,     # % of checkpoints approved
    "rejection_rate": 0.05,    # % of checkpoints rejected
    "hint_rate": 0.03,         # % of checkpoints needing hints
    "avg_duration": 180,       # Average seconds to complete
    "avg_cost": 2.50,          # Average cost per task
}

Target metrics:

Success rate: >90%
Approval rate: >85%
Rejection rate: <10%
Hint rate: <15%

Team collaboration

Assigning reviewers

Match reviewers to task expertise:

{
  "financial_tasks": "[email protected]",
  "marketing_tasks": "[email protected]",
  "technical_tasks": "[email protected]",
  "legal_tasks": "[email protected]"
}

Review response times

Set expectations for checkpoint responses:

Urgent

< 5 minutesFinancial transactions, customer communications

Normal

< 1 hourResearch, analysis, reports

Low Priority

< 24 hoursBaseline tests, experiments

Export evaluation results for team review:

# Get all evaluation results
curl https://app.mixus.ai/api/eval/results \
  -H "Authorization: Bearer mxs_eval_YOUR_KEY" \
  > team_eval_results.json

# Share with team

Common pitfalls

Avoid these mistakes

❌ Vague task descriptions

Problem: “Research AI companies”Solution: “Research OpenAI, Anthropic, Google DeepMind pricing”

❌ Too many checkpoints

Problem: 8 checkpoints for simple calculationSolution: 1-2 checkpoints, or use auto-detect

❌ No baseline comparison

Problem: Only testing with verificationSolution: Run same task without verification for comparison

❌ Ignoring metrics

Problem: Not tracking success/failure ratesSolution: Monitor dashboard, export metrics regularly

❌ Not using hints

Problem: Rejecting when small adjustment would workSolution: Use hint: to guide agent to correct approach

Advanced patterns

Chained evaluations

Run sequential tasks where output of one feeds into next:

# Task 1: Research
result1 = submit_task({
    "taskName": "Research Phase",
    "taskDescription": "Research competitor pricing"
})

execution_id_1 = result1["executionId"]
wait_for_completion(execution_id_1)

# Get Task 1 results
results_1 = get_execution_results(execution_id_1)

# Task 2: Analysis (using Task 1 results)
result2 = submit_task({
    "taskName": "Analysis Phase",
    "taskDescription": f"Analyze pricing data: {results_1['output']}"
})

Conditional workflows

Run different tasks based on results:

result = submit_task({
    "taskName": "Check Inventory",
    "taskDescription": "Check if Product X has inventory > 100"
})

wait_for_completion(result["executionId"])
inventory_result = get_results(result["executionId"])

if inventory_result["inventory"] > 100:
    # High inventory - run discount campaign task
    submit_task({
        "taskName": "Discount Campaign",
        "taskDescription": "Create 20% discount campaign for Product X"
    })
else:
    # Low inventory - run restock task
    submit_task({
        "taskName": "Restock Alert",
        "taskDescription": "Send restock alert to procurement team"
    })

Template-based tasks

Create reusable task templates:

TASK_TEMPLATES = {
    "commission_calc": {
        "taskName": "Commission Calculation",
        "taskDescription": "Calculate {rate}% commission on ${amount}",
        "autoDetectCheckpoints": true,
        "testMode": "with-verification"
    },
    "competitor_research": {
        "taskName": "Competitor Research",
        "taskDescription": "Research {company} pricing and features",
        "autoDetectCheckpoints": true,
        "testMode": "with-verification"
    }
}

# Use template
task = TASK_TEMPLATES["commission_calc"].copy()
task["taskDescription"] = task["taskDescription"].format(
    rate=15,
    amount=50000
)
submit_task(task)

Troubleshooting

Task fails immediately

Check task description

Ensure it’s clear and actionable

Verify reviewer exists

Reviewer must be registered mixus user

Check API key permissions

Needs eval:create scope

Review error message

Look in response or dashboard for details

Checkpoint not triggering

Possible causes:

Auto-detect didn’t identify action as high-risk
Task description too vague
Action type not in checkpoint criteria

Solution: Use manual checkpoints to force verification at specific points.

Slow verification responses

Tips for faster reviews:

Use mobile notifications
Set up Slack alerts
Assign backup reviewers
Use webhooks for real-time notifications

Next steps

Examples

See 10+ ready-to-use tasks

API Reference

Complete API documentation

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Task Preparation

Write better tasks

Getting Started

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API Reference

​Writing effective tasks

​Start with clear objectives

​Use progressive complexity

​Checkpoint strategies

​When to use auto-detect

​When to use manual checkpoints

​Optimal checkpoint placement

​Verification workflow tips

​Responding to checkpoints

approve

reject

hint: [text]

​Using hints effectively

​Testing strategies

​Baseline comparisons

​A/B testing checkpoints

​Performance optimization

​Batch processing

​Webhook vs polling

Use Webhooks

Use Polling

​Optimizing task descriptions

​Cost optimization

​Understanding costs

​Reducing costs

​Quality assurance

​Validation strategies

​Tracking quality metrics

​Team collaboration

​Assigning reviewers

​Review response times

Urgent

Normal

Low Priority

​Sharing results

​Common pitfalls

​Avoid these mistakes

​Advanced patterns

​Chained evaluations

​Conditional workflows

​Template-based tasks

​Troubleshooting

​Task fails immediately

​Checkpoint not triggering

​Slow verification responses

​Next steps

Examples

API Reference

Get Started

Task Preparation

Writing effective tasks

Start with clear objectives

Use progressive complexity

Checkpoint strategies

When to use auto-detect

When to use manual checkpoints

Optimal checkpoint placement

Verification workflow tips

Responding to checkpoints

Using hints effectively

Testing strategies

Baseline comparisons

A/B testing checkpoints

Performance optimization

Batch processing

Webhook vs polling

Optimizing task descriptions

Cost optimization

Understanding costs

Reducing costs

Quality assurance

Validation strategies

Tracking quality metrics

Team collaboration

Assigning reviewers

Review response times

Sharing results

Common pitfalls

Avoid these mistakes

Advanced patterns

Chained evaluations

Conditional workflows

Template-based tasks

Troubleshooting

Task fails immediately

Checkpoint not triggering

Slow verification responses

Next steps