[feature]: Multi-Agent Collaboration Layer for Existing Plane AI Agents

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Summary

Summary

This is a feature extension proposal for the existing Plane AI Agents system.

The idea is to introduce a multi-agent collaboration layer where existing agents can interact with each other, collaborate on tasks, and operate as a coordinated team inside issues and workspaces.

This builds on the current Agent capabilities by enabling structured agent-to-agent workflows and collaboration patterns.

Problem

Current AI agent implementations in work management systems focus on single-agent execution per task.

However, real-world workflows often require:

• multiple specialized roles working together
• decomposition of complex tasks into sub-problems
• iterative refinement between different perspectives (e.g. research, development, QA)

Without multi-agent coordination, agents operate in isolation and cannot simulate real team behavior.

Proposed Extension

Enhance the existing AI Agents system with a collaboration layer that enables:

1. Agent-to-Agent Communication

• Agents can exchange messages within an issue thread or internal context channel
• Each agent can respond based on its role and tool access

2. Multi-Agent Task Execution

• A single issue can involve multiple assigned agents
• Agents can divide responsibilities automatically or via human assignment

3. Structured Collaboration Roles

Example roles:

• Research Agent → gathers context and explores solutions
• Developer Agent → proposes implementation
• QA Agent → evaluates risks and edge cases
• Planner Agent → breaks down tasks into steps

4. Agent Handover System

• One agent can pass context or subtasks to another agent
• Maintains structured workflow between agents

5. Human + Multi-Agent Oversight

• Humans remain in control of final decisions
• Agents act as collaborative assistants, not autonomous decision-makers

Use Cases

• Complex feature planning with multiple perspectives
• Bug investigation involving research + debugging + validation
• Automatic breakdown of feature requests into sub-tasks
• Documentation generation with iterative review
• Cross-checking solutions between multiple AI roles

Benefits

• More realistic simulation of real engineering teams
• Better handling of complex multi-step problems
• Reduced human workload in coordination tasks
• More structured and reliable AI output
• Extends existing Agents into a true “AI workforce layer”

Alignment with Existing System

This proposal does NOT replace existing Agents.

Instead, it extends them by adding:

• collaboration between agents
• shared context execution
• multi-agent orchestration on top of current Agent infrastructure

Future Direction

This could evolve into:

• Agent workflows (graph-based execution)
• Agent marketplace (specialized agents)
• Persistent agent teams per workspace
• Automated sprint execution via agent groups

Why should this be worked on?

Current AI agents in Plane operate in isolation, which limits their ability to handle complex, multi-step, real-world workflows that typically require collaboration between different roles.

Many tasks inside a workspace naturally require multiple perspectives, such as research, implementation, QA, and planning. Without a collaboration layer between agents, users must manually coordinate these steps themselves.

A multi-agent collaboration extension would allow agents to work together on the same issue, exchange context, and divide responsibilities automatically. This would reduce manual coordination, improve output quality, and better reflect how real engineering teams operate.

It would also help break down complex issues into structured workflows, speed up execution, and make AI assistance more practical for larger projects.