Assign AI Agents to Automate Workflows

Deploy and configure AI agents to automate issue resolution, code review, and workflow management

AI agents in PraisonAI Platform automate repetitive tasks, analyze issues, and assist with decision-making, enabling teams to focus on high-value work.

python

from praisonaiagents import Agent

agent = Agent(
    name="Issue Worker",
    instructions="Analyse and resolve assigned platform issues.",
)

agent.start("Review issue #42 and propose a fix.")

The user registers the agent on the platform, assigns an issue, and reviews its output in the workspace.

Diagram

Agent Types & Use Cases

Code Analysis Agents

Perfect for code review, security analysis, and technical debt identification:

Create Code Review Agent

python

import asyncio
from praisonai_platform.client import PlatformClient

async def create_code_review_agent():
    client = PlatformClient("http://localhost:8000", token="your-jwt-token")
    ws_id = "your-workspace-id"

    agent = await client.create_agent(
        ws_id,
        name="Senior Code Reviewer",
        description="AI agent specialized in code review and quality analysis",
        instructions="""
        You are a senior software engineer with expertise in code review.

        When assigned to an issue:
        1. Analyze any code snippets or repository links provided
        2. Check for common issues: security vulnerabilities, performance problems, code smells
        3. Review for best practices: SOLID principles, clean code, proper error handling
        4. Suggest specific improvements with code examples
        5. Rate severity: critical, high, medium, low
        6. Add your review as a structured comment

        Format your response as:
        ## Code Review Analysis
        **Severity**: [level]
        **Issues Found**: [number]

        ### Critical Issues
        - [specific issue with line numbers if available]

        ### Suggestions
        - [actionable recommendations]

        ### Code Examples
        # [language] example
        # Improved version of the suggested code
        """,
        model="gpt-4o",
        auto_assign_labels=["code-reviewed", "ai-analyzed"],
        triggers={
            "on_assign": True,
            "on_label_added": ["needs-review", "pull-request"],
            "on_comment_keywords": ["@code-review", "review please"]
        }
    )

    print(f"✅ Created code review agent: {agent['name']}")
    return agent

code_agent = asyncio.run(create_code_review_agent())

Assign to Code Issues

python

async def assign_code_review():
    client = PlatformClient("http://localhost:8000", token="your-jwt-token")
    ws_id = "your-workspace-id"

    # Create a code-related issue
    code_issue = await client.create_issue(
        ws_id,
        title="Refactor authentication middleware",
        description="""
        Current authentication middleware has performance issues and security concerns.

        Current implementation (simplified):
        # def auth_middleware(request):
        #     token = request.headers.get("Authorization")
        #     if token:
        #         user = decode_token(token)
        #         if user and user.is_active:
        #             request.user = user
        #             return True
        #     return False

        Issues:
        - Database call on every request
        - No token caching
        - Missing rate limiting
        - No proper error handling
        """,
        labels=["backend", "security", "performance", "needs-review"],
        priority="high"
    )

    # Assign the code review agent
    await client.assign_issue_agent(ws_id, code_issue['id'], code_agent['id'])

    print(f"✅ Assigned code review agent to issue {code_issue['identifier']}")
    return code_issue

issue = asyncio.run(assign_code_review())

Bug Triage Agents

Automatically analyze and categorize bug reports:

Create Bug Triage Agent

python

async def create_bug_triage_agent():
    client = PlatformClient("http://localhost:8000", token="your-jwt-token")
    ws_id = "your-workspace-id"

    agent = await client.create_agent(
        ws_id,
        name="Bug Triage Specialist",
        description="Analyzes bug reports and categorizes them for efficient resolution",
        instructions="""
        You are a QA specialist that triages incoming bug reports.

        For each bug report:
        1. Analyze severity based on impact and frequency
        2. Identify the likely component/system affected
        3. Suggest reproduction steps if missing
        4. Recommend initial debugging approach
        5. Assign appropriate priority and labels
        6. Determine if immediate escalation is needed

        Severity levels:
        - Critical: System down, security breach, data loss
        - High: Core functionality broken, many users affected
        - Medium: Feature not working, some users affected  
        - Low: Minor issue, cosmetic problems

        Always add these labels based on analysis:
        - Component: frontend, backend, database, api
        - Priority: critical, high, medium, low
        - Type: crash, performance, ui-bug, data-issue
        """,
        model="gpt-4o-mini",  # Faster model for triage
        auto_assign_labels=["triaged", "ai-categorized"],
        triggers={
            "on_assign": True,
            "on_label_added": ["bug", "issue"],
            "on_status_change": "reported"
        }
    )

    print(f"✅ Created bug triage agent: {agent['name']}")
    return agent

triage_agent = asyncio.run(create_bug_triage_agent())

Auto-Assign to Bug Reports

python

async def setup_auto_bug_triage():
    client = PlatformClient("http://localhost:8000", token="your-jwt-token")
    ws_id = "your-workspace-id"

    # Set up automatic assignment rule
    automation_rule = await client.create_automation_rule(
        ws_id,
        name="Auto Bug Triage",
        description="Automatically assign triage agent to new bug reports",
        triggers=[
            {
                "type": "issue_created",
                "conditions": {
                    "labels_include": ["bug"],
                    "status": "reported"
                }
            }
        ],
        actions=[
            {
                "type": "assign_agent",
                "agent_id": triage_agent['id']
            },
            {
                "type": "add_comment",
                "content": "🤖 Bug triage agent assigned. Analysis in progress..."
            }
        ]
    )

    # Test with a sample bug report
    bug_report = await client.create_issue(
        ws_id,
        title="App crashes when uploading large files",
        description="""
        **Steps to reproduce:**
        1. Go to file upload page
        2. Select file larger than 10MB
        3. Click upload button

        **Expected:** File uploads successfully
        **Actual:** App crashes with white screen

        **Additional info:**
        - Happens on both Chrome and Firefox
        - Only with files >10MB
        - Started after last update
        - Error in console: "Memory limit exceeded"
        """,
        labels=["bug"],
        status="reported"
    )

    print(f"✅ Created bug report {bug_report['identifier']} - agent will auto-assign")
    return automation_rule, bug_report

rule, bug = asyncio.run(setup_auto_bug_triage())

Content Generation Agents

Automate documentation, test cases, and content creation:

Create Documentation Agent

python

async def create_docs_agent():
    client = PlatformClient("http://localhost:8000", token="your-jwt-token")
    ws_id = "your-workspace-id"

    agent = await client.create_agent(
        ws_id,
        name="Documentation Writer",
        description="Generates and updates technical documentation",
        instructions="""
        You are a technical writer that creates clear, comprehensive documentation.

        When assigned to documentation tasks:
        1. Analyze the feature/API that needs documentation
        2. Create structured documentation with:
           - Clear overview and purpose
           - Step-by-step usage instructions
           - Code examples with proper formatting
           - Common use cases and patterns
           - Troubleshooting section
           - Links to related resources

        Follow these standards:
        - Use Markdown formatting
        - Include runnable code examples
        - Add appropriate headers and structure
        - Use clear, jargon-free language
        - Include both basic and advanced usage

        For API documentation, always include:
        - Request/response examples
        - Parameter descriptions
        - Error codes and handling
        - Rate limiting information
        """,
        model="gpt-4o",
        auto_assign_labels=["documented", "ready-for-review"],
        file_access=True,  # Allow reading/writing documentation files
        triggers={
            "on_assign": True,
            "on_label_added": ["needs-docs", "api-change"]
        }
    )

    return agent

docs_agent = asyncio.run(create_docs_agent())

Generate API Documentation

python

async def generate_api_docs():
    client = PlatformClient("http://localhost:8000", token="your-jwt-token")
    ws_id = "your-workspace-id"

    # Create documentation request
    docs_issue = await client.create_issue(
        ws_id,
        title="Document new webhook API endpoints",
        description="""
        New webhook API endpoints need comprehensive documentation:

        **New Endpoints:**
        - POST /api/v1/webhooks - Create webhook
        - GET /api/v1/webhooks - List webhooks  
        - PUT /api/v1/webhooks/{id} - Update webhook
        - DELETE /api/v1/webhooks/{id} - Delete webhook

        **Requirements:**
        - Include request/response schemas
        - Add authentication examples
        - Document webhook event types
        - Provide testing instructions
        - Add troubleshooting guide

        **Target Audience:** External developers integrating with our API
        """,
        labels=["documentation", "api", "needs-docs"],
        priority="medium",
        assignee_type="agent",
        assignee_id=docs_agent['id']
    )

    print(f"✅ Documentation request created: {docs_issue['identifier']}")

    # Add additional context for the agent
    await client.add_issue_comment(
        ws_id,
        docs_issue['id'],
        """
        Additional context for documentation:

        **Webhook Event Types:**
        - issue.created, issue.updated, issue.deleted
        - project.created, project.updated
        - agent.assigned, agent.completed

        **Authentication:** Bearer token required
        **Rate Limits:** 100 requests/hour per webhook
        **Payload Size:** Maximum 1MB per webhook call
        """
    )

    return docs_issue

docs_task = asyncio.run(generate_api_docs())

Advanced Agent Configuration

Multi-Agent Workflows

Set up agents that work together for complex tasks:

python

async def create_multi_agent_workflow():
    client = PlatformClient("http://localhost:8000", token="your-jwt-token")
    ws_id = "your-workspace-id"

    # Agent 1: Initial Analysis
    analyzer = await client.create_agent(
        ws_id,
        name="Issue Analyzer",
        description="Analyzes issues and determines next steps",
        instructions="""
        Analyze the issue and determine what type of work is needed:
        - If it's a bug: assign to Bug Specialist
        - If it's a feature: assign to Feature Planner  
        - If it needs research: assign to Research Agent
        - If it's documentation: assign to Docs Writer

        Add appropriate labels and hand off to the right specialist.
        """,
        auto_assign_labels=["analyzed"],
        handoff_agents={
            "bug": "bug-specialist-id",
            "feature": "feature-planner-id", 
            "research": "research-agent-id",
            "docs": "docs-writer-id"
        }
    )

    # Agent 2: Bug Specialist
    bug_specialist = await client.create_agent(
        ws_id,
        name="Bug Specialist",
        description="Deep bug analysis and resolution planning",
        instructions="""
        Perform deep analysis of bugs:
        1. Create reproduction steps
        2. Identify root cause
        3. Suggest fix approach
        4. Estimate complexity
        5. Create subtasks if needed
        6. Hand back to human developer with detailed plan
        """,
        auto_assign_labels=["bug-analyzed", "ready-for-dev"]
    )

    # Set up workflow triggers
    workflow = await client.create_workflow(
        ws_id,
        name="Issue Processing Pipeline",
        steps=[
            {
                "agent": analyzer['id'],
                "triggers": ["issue.created", "issue.labeled:needs-analysis"],
                "next_step_conditions": {
                    "if_labels_include": ["bug"],
                    "then_assign": bug_specialist['id']
                }
            },
            {
                "agent": bug_specialist['id'],
                "triggers": ["agent.handoff"],
                "completion_actions": [
                    {"type": "notify_assignee"},
                    {"type": "update_status", "status": "ready-for-development"}
                ]
            }
        ]
    )

    print(f"✅ Multi-agent workflow created: {workflow['name']}")
    return analyzer, bug_specialist, workflow

Agent Performance Monitoring

Track and optimize agent performance:

python

async def monitor_agent_performance():
    client = PlatformClient("http://localhost:8000", token="your-jwt-token")
    ws_id = "your-workspace-id"

    # Get agent metrics
    agents = await client.list_agents(ws_id)

    for agent in agents:
        metrics = await client.get_agent_metrics(
            ws_id, 
            agent['id'],
            time_range="last_30_days"
        )

        print(f"\n📊 {agent['name']} Performance:")
        print(f"   Tasks completed: {metrics['tasks_completed']}")
        print(f"   Average response time: {metrics['avg_response_time']}s")
        print(f"   Success rate: {metrics['success_rate']}%")
        print(f"   User satisfaction: {metrics['satisfaction_score']}/5")

        # Identify improvement areas
        if metrics['success_rate'] < 0.8:
            print(f"   ⚠️ Low success rate - review instructions")

        if metrics['avg_response_time'] > 30:
            print(f"   ⚠️ Slow response - consider faster model")

        # Get recent failures for analysis
        if metrics['recent_failures']:
            print(f"   🔍 Recent failures: {len(metrics['recent_failures'])}")
            for failure in metrics['recent_failures'][:3]:
                print(f"      - {failure['issue']}: {failure['error'][:50]}...")

asyncio.run(monitor_agent_performance())

Agent Best Practices

Writing Effective Instructions

Clear Role Definition:

python

# Good: Specific role and expertise
instructions = """
You are a senior DevOps engineer with expertise in containerization and CI/CD.
Your role is to analyze deployment issues and recommend infrastructure solutions.
"""

# Avoid: Vague or overly broad role
instructions = "You help with technical issues."

Structured Output Format:

python

instructions = """
Always format your response as:

## Analysis
[Your analysis here]

## Recommendations  
1. [Specific action]
2. [Another action]

## Next Steps
- [ ] [Actionable task]
- [ ] [Another task]
"""

Model Selection Strategy

Task Complexity Mapping:

python

model_selection = {
    "simple_triage": "gpt-4o-mini",     # Fast, cost-effective
    "code_review": "gpt-4o",            # High accuracy needed
    "documentation": "gpt-4o",          # Quality important
    "data_analysis": "gpt-4o",          # Complex reasoning
    "chat_support": "gpt-4o-mini",      # Quick responses
}

# Select based on task requirements
agent = await client.create_agent(
    ws_id,
    name="Bug Triager",
    model=model_selection["simple_triage"],
    # ... other config
)

Trigger Configuration

Smart Triggering:

python

triggers = {
    # Immediate action for critical issues
    "on_label_added": ["critical", "security"],

    # Batch processing for efficiency
    "on_schedule": "0 9 * * 1-5",  # Weekdays at 9 AM

    # Conditional triggers
    "on_comment_keywords": ["@agent", "help needed"],

    # Status-based triggers
    "on_status_change": ["reported", "needs-review"]
}

Avoiding Trigger Loops:

python

# Prevent infinite loops
agent_config = {
    "triggers": {
        "on_label_added": ["needs-analysis"]
    },
    "auto_assign_labels": ["analyzed"],  # Different label
    "ignore_own_updates": True,  # Don't trigger on own changes
    "cooldown_period": "5m"  # Wait 5 minutes between runs
}

Error Handling & Recovery

Graceful Degradation:

python

agent_instructions = """
If you cannot complete the full analysis:
1. Provide what information you can gather
2. Clearly state what's missing or unclear
3. Suggest specific next steps for humans
4. Add the 'needs-human-review' label
5. Do not guess or make assumptions about missing information
"""

# Configure retry behavior
agent = await client.create_agent(
    ws_id,
    name="Robust Agent",
    max_retries=3,
    retry_delay="1m",
    fallback_action="notify_human",
    error_labels=["agent-failed", "needs-manual-review"]
)

Testing Agent Workflows

Validate agent behavior before deployment:

python

async def test_agent_workflow():
    client = PlatformClient("http://localhost:8000", token="your-jwt-token")
    ws_id = "your-workspace-id"

    # Create test workspace for agent testing
    test_ws = await client.create_workspace(
        name="Agent Testing",
        description="Sandbox for testing agent configurations"
    )
    test_ws_id = test_ws['id']

    # Deploy agent to test workspace
    test_agent = await client.create_agent(
        test_ws_id,
        name="Test Code Reviewer",
        # ... agent configuration
    )

    # Create test scenarios
    test_scenarios = [
        {
            "name": "Simple Bug Report",
            "issue": {
                "title": "Button not clickable on mobile",
                "description": "The submit button doesn't respond to taps on iOS Safari",
                "labels": ["bug", "mobile"]
            },
            "expected_labels": ["triaged", "ui-bug", "mobile"],
            "expected_priority": "medium"
        },
        {
            "name": "Security Issue",
            "issue": {
                "title": "SQL injection vulnerability in search",
                "description": "User input not sanitized in search endpoint",
                "labels": ["bug", "security"]
            },
            "expected_labels": ["triaged", "security", "critical"],
            "expected_priority": "critical"
        }
    ]

    # Run test scenarios
    results = []
    for scenario in test_scenarios:
        print(f"🧪 Testing: {scenario['name']}")

        # Create test issue
        test_issue = await client.create_issue(test_ws_id, **scenario['issue'])

        # Assign agent
        await client.assign_issue_agent(test_ws_id, test_issue['id'], test_agent['id'])

        # Wait for processing
        await asyncio.sleep(5)

        # Check results
        updated_issue = await client.get_issue(test_ws_id, test_issue['id'])

        test_result = {
            "scenario": scenario['name'],
            "passed": all(label in updated_issue['labels'] for label in scenario['expected_labels']),
            "actual_labels": updated_issue['labels'],
            "expected_labels": scenario['expected_labels']
        }

        results.append(test_result)
        print(f"   {'✅ PASS' if test_result['passed'] else '❌ FAIL'}")

    # Cleanup test workspace
    await client.delete_workspace(test_ws_id)

    return results

test_results = asyncio.run(test_agent_workflow())
print(f"\nTest Summary: {sum(1 for r in test_results if r['passed'])}/{len(test_results)} passed")

Related Guides

Issue Organization

Structure work for optimal agent assignment

Platform API

Complete agent management API reference

Workflow Automation

Advanced automation and orchestration

Integration Patterns

Common integration and deployment patterns