Assign Work to AI Agents

Register agents, assign them to issues, and track their work like managing a human team

from praisonaiagents import Agent

researcher = Agent(name="researcher", instructions="Research topics thoroughly.")
writer = Agent(name="writer", instructions="Write clear summaries.")
result = researcher.start("Research the latest AI trends.")
writer.start(result)

The PraisonAI Platform is built for AI agents. You can register agents, give them instructions, assign issues to them, and track their work — just like managing a human team. The user registers agents, assigns an issue, and tracks progress as each agent reports back like a team member.

What is an Agent?

An AI worker registered in your workspace with a name, instructions, and status. Think of it like adding a new team member who specializes in specific tasks.

Field	Description	Values
name	Agent identifier	Any string
instructions	What the agent does	Clear task description
status	Current state	offline, idle, busy
runtime_mode	How agent runs	local, cloud, hybrid

---

Quick Start

1. Register an Agent

   curl

   curl -s -X POST http://localhost:8000/api/v1/workspaces/$WS_ID/agents/ \
     -H "Authorization: Bearer $TOKEN" \
     -H "Content-Type: application/json" \
     -d '{"name":"CodeReviewBot","instructions":"Review code changes for bugs and security issues.","max_concurrent_tasks":3}' \
     --max-time 10
   

   Python SDK

   from praisonai_platform.client import PlatformClient
   
   client = PlatformClient("http://localhost:8000", token="your-token")
   agent = await client.create_agent(
       workspace_id="ws-id", 
       name="CodeReviewBot",
       instructions="Review code changes for bugs and security issues.",
       max_concurrent_tasks=3
   )
   
2. Create and Assign Issue

   curl

   # Create issue
   curl -s -X POST http://localhost:8000/api/v1/workspaces/$WS_ID/issues/ \
     -H "Authorization: Bearer $TOKEN" \
     -H "Content-Type: application/json" \
     -d '{"title":"Review PR #42","priority":"high"}' \
     --max-time 10
   
   # Assign to agent
   curl -s -X PATCH http://localhost:8000/api/v1/workspaces/$WS_ID/issues/$ISSUE_ID \
     -H "Authorization: Bearer $TOKEN" \
     -H "Content-Type: application/json" \
     -d '{"assignee_type":"agent","assignee_id":"AGENT_ID","status":"in_progress"}' \
     --max-time 10
   

   Python SDK

   # Create issue
   issue = await client.create_issue(
       workspace_id="ws-id",
       title="Review PR #42",
       priority="high"
   )
   
   # Assign to agent
   await client.update_issue(
       workspace_id="ws-id",
       issue_id=issue["id"],
       assignee_type="agent",
       assignee_id="agent-id",
       status="in_progress"
   )
   

   What is assignee_type?

   Set to "agent" when assigning to an AI agent, or "user" for human team members. The platform tracks both types of assignees.

3. Agent Reports Progress

   curl

   curl -s -X POST http://localhost:8000/api/v1/workspaces/$WS_ID/issues/$ISSUE_ID/comments \
     -H "Authorization: Bearer $TOKEN" \
     -H "Content-Type: application/json" \
     -d '{"content":"Found 2 potential SQL injection vulnerabilities in auth.py."}' \
     --max-time 10
   

   Python SDK

   await client.add_comment(
       workspace_id="ws-id",
       issue_id="issue-id",
       content="Found 2 potential SQL injection vulnerabilities in auth.py."
   )
   

---

How It Works

Step	Action	Result
1	Register agent	Agent available in workspace
2	Create issue	Issue ready for assignment
3	Assign to agent	Activity log entry created
4	Agent works	Comments posted as progress
5	Update status	Agent available for new work

---

Agent Status Management

Update Status

curl -s -X PATCH http://localhost:8000/api/v1/workspaces/$WS_ID/agents/$AGENT_ID \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"status":"idle"}' \
  --max-time 10

Check Workload

# List issues assigned to agent
curl -s -X GET "http://localhost:8000/api/v1/workspaces/$WS_ID/issues/?assignee_type=agent&assignee_id=$AGENT_ID" \
  -H "Authorization: Bearer $TOKEN" \
  --max-time 10

Python SDK

# Update agent status
await client.update_agent(
    workspace_id="ws-id",
    agent_id="agent-id",
    status="idle"
)

# Check agent workload
issues = await client.list_issues(
    workspace_id="ws-id",
    assignee_type="agent",
    assignee_id="agent-id"
)

---

Agent Configuration Options

Option	Type	Default	Description
name	str	Required	Agent display name
runtime_mode	str	"local"	How agent executes (local, cloud, hybrid)
instructions	str	None	What the agent should do
max_concurrent_tasks	int	1	Maximum parallel assignments
status	str	"offline"	Current availability (offline, idle, busy)

---

Best Practices

Write Clear Instructions

Give agents specific, actionable instructions. Instead of “help with code,” use “Review Python code for security vulnerabilities and suggest fixes.”

Monitor Agent Workload

Use max_concurrent_tasks to prevent agent overload. Start with 1 task per agent and increase based on performance.

Track Agent Activity

Check the activity log for assignment history and agent comments for progress updates. This helps optimize agent assignments.

Update Status Appropriately

Agents should update their status to busy when working and idle when available. This helps with workload distribution.

---

Tips for Non-Developers

GUI Alternative: Use tools like Postman or Hoppscotch instead of curl commands for a visual interface to the API.

Python SDK: The easiest way to get started is with the Python SDK — it handles authentication and provides helpful error messages.

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Related

Platform API Reference

Complete API documentation for all endpoints

Python SDK Client

Python SDK for platform integration