Autonomous Execution

Autonomous Execution: Agents That Work for Days

Auteryn agents can run autonomously for extended periods, handling complex multi-step workflows without human intervention. Tasks can persist for up to one week with automatic resumption.

Market Leader: Auteryn supports the longest task persistence in the market (up to 1 week with automatic resumption). Single executions run for up to 2 hours in DEEP mode. Competitors are limited to session-based (Kimi), task-based (Devin), or unclear durations (Manus).

What is Autonomous Execution?

Autonomous execution means agents can:

Plan multi-step workflows independently
Execute tasks without constant supervision
Recover from errors automatically
Adapt based on results
Persist state across sessions
Report progress in real-time

Think of it as hiring a virtual employee who works 24/7 without breaks.

FAST vs DEEP Modes

Auteryn provides two execution modes optimized for different use cases:

FAST Mode

Best for: Quick responses, simple tasks, customer support

Aspect	Specification
Model	Gemini 3 Flash
Thinking Budget	1,024 tokens
Response Time	<2 seconds (TTFT)
Max LLM Calls	500 per run
Timeout	11 minutes
Use Cases	Q&A, simple automation, chat

Example Use Cases:

Customer support chatbots
FAQ answering
Simple data lookups
Quick calculations
Form filling

DEEP Mode

Best for: Complex research, multi-step workflows, extended reasoning

Aspect	Specification
Model	Gemini 3 Pro
Thinking Budget	16,384 tokens
Response Time	Seconds to minutes
Max LLM Calls	5,000 per run
Timeout	2 hours per execution
Use Cases	Research, analysis, complex automation

Example Use Cases:

Competitive research
Code review and refactoring
Report generation
Data analysis
Multi-step testing

Smart Routing

Auteryn automatically routes requests to the appropriate mode:

Request → Pattern Analysis → AI Classification → FAST or DEEP

Automatically routed to FAST:

“What is X?”
“Explain Y”
“Hello”
“Help me with Z”
Simple questions
Quick lookups

Manual Override

Force a specific mode:

# Force FAST mode
agent.run("Complex task", mode="fast")

# Force DEEP mode
agent.run("Simple task", mode="deep")

# Auto-routing (default)
agent.run("Any task", mode="auto")

Extended Reasoning

Thinking Budget

The thinking budget controls how much the agent can “think” before responding:

FAST Mode (1,024 tokens):

Quick reasoning
Minimal planning
Direct answers

DEEP Mode (16,384 tokens):

Extended reasoning
Detailed planning
Thorough analysis

Thought Streaming

Watch your agent think in real-time:

# Enable thought streaming
agent.configure(include_thoughts=True)

# Agent's thoughts are streamed to UI
# "Let me break this down..."
# "First, I need to research..."
# "Based on the data, I should..."

Benefits:

Understand agent reasoning
Debug decision-making
Build trust with transparency
Improve prompts based on thoughts

Long-Running Workflows

Extended Task Persistence

Auteryn agents support task persistence for up to 7 days with automatic resumption. Each execution runs for up to 2 hours (DEEP mode), and tasks automatically resume if interrupted:

# Start long-running workflow
task = agent.run_async("""
Comprehensive competitor analysis:

1. Research 10 competitors
2. Analyze their products, pricing, features
3. Create comparison spreadsheet
4. Generate SWOT analysis for each
5. Create presentation with findings
6. Email report to team

This may take several days. Work autonomously.
""")

# Check progress anytime
status = task.status()
# "running" - Day 2 of 7, Step 3/6 complete

# Get result when done
result = task.result()  # Blocks until complete

State Persistence

Agents maintain state across sessions:

Scratchpad - Working memory for findings
Progress tracking - Step completion status
File system - Generated files persist
Context - Conversation history maintained

Error Recovery

Agents automatically recover from transient failures:

# Agent encounters API rate limit
# → Waits and retries automatically

# Agent loses network connection
# → Reconnects and continues

# Agent runs out of disk space
# → Cleans up temp files and continues

Task Planning & Progress

Create Task Plans

Agents create structured plans for complex tasks:

plan = agent.create_task_plan(
    description="Research competitors",
    steps=[
        {"description": "Search for Competitor A", "depends_on": []},
        {"description": "Search for Competitor B", "depends_on": []},
        {"description": "Search for Competitor C", "depends_on": []},
        {"description": "Compare results", "depends_on": [1, 2, 3]}
    ]
)

# Steps 1, 2, 3 run in PARALLEL
# Step 4 waits for all three to complete

Track Progress

Monitor execution in real-time:

# Get current progress
progress = agent.get_task_progress()
# {
#   "plan_id": "plan_123",
#   "status": "in_progress",
#   "current_step": 2,
#   "total_steps": 6,
#   "steps": [
#     {"id": 1, "status": "completed", "result": "Found 3 competitors"},
#     {"id": 2, "status": "in_progress", "result": null},
#     ...
#   ]
# }

Progress Events

Receive real-time updates via SSE:

// Frontend receives progress events
eventSource.addEventListener('task_progress', (event) => {
  const progress = JSON.parse(event.data);
  console.log(`Step ${progress.current_step}/${progress.total_steps}`);
  console.log(`Status: ${progress.status}`);
});

Parallel Tool Execution

Concurrent Operations

Agents can execute multiple tools simultaneously:

# Agent executes these in PARALLEL (not sequential)
results = agent.run("""
Research three competitors simultaneously:
- Search for Competitor A
- Search for Competitor B
- Search for Competitor C
""")

# 3x faster than sequential execution

Dependency Management

Define explicit dependencies:

plan = agent.create_task_plan(
    steps=[
        {"id": 1, "description": "Fetch data", "depends_on": []},
        {"id": 2, "description": "Clean data", "depends_on": [1]},
        {"id": 3, "description": "Analyze data", "depends_on": [2]},
        {"id": 4, "description": "Generate charts", "depends_on": [3]},
        {"id": 5, "description": "Create report", "depends_on": [3, 4]}
    ]
)

# Execution order:
# 1 → 2 → 3 → (4 and 5 in parallel)

Human-in-the-Loop

Request Approval

Agents can request human approval for critical decisions:

# Agent requests approval
approved = agent.request_approval(
    message="About to delete 1,000 records. Proceed?",
    options=["Yes, proceed", "No, cancel", "Show me the records first"]
)

if approved == "Yes, proceed":
    agent.run("delete_records.py")

Request Input

Get information from humans:

# Request text input
email = agent.request_input(
    message="What email should I send the report to?",
    input_type="text",
    validation="email"
)

# Request choice
priority = agent.request_input(
    message="What priority should this ticket have?",
    input_type="choice",
    options=["Low", "Medium", "High", "Critical"]
)

Comparison with Competitors

Task Persistence

Platform	Max Task Duration	Execution Timeout	Auto-Resume
Auteryn	✅ Up to 1 week	2 hours (DEEP)	✅ Yes
Kimi K2.5	⚠️ Session-based	Varies	❌ No
Manus AI	⚠️ Background	Unclear	⚠️ Unknown
Devin AI	⚠️ Task-based	Hours	⚠️ Unknown

Execution Modes

Platform	Modes	Thinking Budget
Auteryn	FAST + DEEP	1K / 16K tokens
Kimi K2.5	4 modes	Varies
Manus AI	Single mode	N/A
Devin AI	Single mode	N/A

Tool Ecosystem

Platform	Built-In Tools	Parallel Execution
Auteryn	✅ 50+ tools	✅ Yes
Kimi K2.5	⚠️ ~10 tools	✅ Yes (Agent Swarm)
Manus AI	⚠️ ~5 tools	❌ No
Devin AI	⚠️ ~10 tools	❌ No

Use Cases

Multi-Day Research Project

agent.run("""
Comprehensive market research project (3-5 days):

Day 1: Research 20 competitors
- Gather product information
- Analyze pricing strategies
- Review customer feedback

Day 2-3: Deep analysis
- SWOT analysis for each competitor
- Market positioning analysis
- Feature comparison matrix

Day 4: Report creation
- Generate charts and visualizations
- Create PowerPoint presentation
- Write executive summary

Day 5: Distribution
- Email report to stakeholders
- Post summary to Slack
- Update Confluence page

Work autonomously. Report progress daily.
""")

Continuous Monitoring

agent.run("""
Monitor competitor websites continuously (1 week):

Every 6 hours:
1. Check competitor pricing pages
2. Screenshot any changes
3. Analyze differences
4. Alert team if significant changes

Run for 7 days, then generate summary report.
""")

Best Practices

For Long-Running Tasks

Do:

Use DEEP mode for complex workflows
Enable thought streaming for visibility
Create task plans with clear steps
Save findings to scratchpad regularly
Set up progress notifications

Don’t:

Use FAST mode for multi-day tasks
Skip error handling
Forget to save intermediate results
Ignore resource limits
Run without monitoring

For Quick Tasks

Do:

Use FAST mode for simple queries
Keep prompts concise
Use auto-routing for flexibility

Don’t:

Force DEEP mode for simple tasks
Over-complicate prompts
Expect extended reasoning in FAST mode

Monitoring & Observability

Real-Time Progress

Track agent execution:

# Subscribe to progress events
agent.on('progress', lambda event: {
    print(f"Step {event.current_step}/{event.total_steps}")
    print(f"Status: {event.status}")
    print(f"Result: {event.result}")
})

Execution Logs

Access detailed logs:

# Get execution logs
logs = agent.get_logs(
    run_id="run_123",
    level="info"  # "debug", "info", "warning", "error"
)

# Stream logs in real-time
for log in agent.stream_logs(run_id="run_123"):
    print(log)

Performance Metrics

Monitor agent performance:

metrics = agent.get_metrics(run_id="run_123")
# {
#   "duration": "2h 15m 30s",
#   "llm_calls": 156,
#   "tool_calls": 89,
#   "tokens_used": 125000,
#   "credits_consumed": 450
# }

Cost Optimization

Choose the Right Mode

Task Type	Recommended Mode	Cost
Simple Q&A	FAST	Low
Customer support	FAST	Low
Data lookup	FAST	Low
Research	DEEP	Medium
Analysis	DEEP	Medium
Multi-day projects	DEEP	High

Monitor Credit Usage

# Check credit consumption
usage = agent.get_credit_usage(run_id="run_123")
# {
#   "total_credits": 450,
#   "breakdown": {
#     "llm_calls": 300,
#     "tool_calls": 100,
#     "storage": 50
#   }
# }

API Reference

Execution Control

# Synchronous execution
result = agent.run("Task description", mode="auto")

# Asynchronous execution
task = agent.run_async("Long task", mode="deep")

# Check status
status = task.status()  # "pending", "running", "completed", "failed"

# Get result
result = task.result()  # Blocks until complete

# Cancel execution
task.cancel()

Configuration

# Configure execution settings
agent.configure(
    mode="auto",  # "fast", "deep", "auto"
    include_thoughts=True,  # Stream thinking process
    max_duration="1w",  # Maximum runtime
    auto_retry=True  # Retry on transient failures
)

Resources

Questions?

How long can agents run? Single executions run for up to 2 hours (DEEP mode). Tasks can persist for up to 1 week with automatic resumption.
What happens if an agent times out? It saves progress and automatically resumes from where it left off.
Can I stop a running agent? Yes, anytime via UI or API.
How do I choose FAST vs DEEP? Use auto-routing (default) or force a specific mode.
What’s the cost difference? DEEP mode uses more credits due to extended reasoning and longer execution time.

View all FAQs →