Agent Team user guide

Introduction

Agent Team is a core collaboration feature of the JiuwenSwarm platform. It lets multiple agents form a team around one goal and complete complex work together. Unlike boosting a single agent’s capability, Agent Team emphasizes teamwork, division of labor, and continuous delivery.

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Concepts

1.1 Positioning of Agent Team

Agent Team is not “a stronger single agent”—it is multi-agent teamwork.

Imagine you need to finish a complex task—for example, “deep-dive an industry and produce an analysis report.” That work spans multiple phases:

• research and information gathering
• data analysis and structuring
• writing and layout
• review and polish

If you rely on one agent, it must master research, analysis, writing, and proofreading at once, and run steps strictly one after another, which is inefficient.

Agent Team’s idea is: let specialized agents form a team; each handles what it does best, and they complete the job together.

Analogy: Like a project team—a lead coordinates, researchers gather facts, analysts process data, and writers produce the report. Everyone focuses on their role; the team delivers the outcome.

1.2 Why use Agent Team?

Limits of a single agent:

1. Hard to cover many phases well — one agent rarely excels at research, analysis, execution, and proofreading all at once
2. Serial execution is slow — work must proceed step by step with little parallelism
3. Long chains are error-prone — omissions and drift accumulate
4. Hard to improve continuously — outputs from each phase are hard for other phases to reuse and refine in time

Benefits of Agent Team:

1. Specialization — each agent focuses on its strength; higher quality per phase
2. Parallelism — multiple agents can work at once and lift throughput
3. Handoffs — later steps consume earlier outputs directly
4. Continuous delivery — the team pushes until the final goal is met

1.3 From Harness Engineering to Coordination Engineering

Traditional agent development often focuses on Harness Engineering (harnessing a single agent):

• Making one agent more capable
• Better prompts
• Richer tools

Agent Team shifts toward Coordination Engineering (orchestrating collaboration):

• How multiple agents divide work
• Collaboration mechanics and process
• Task flow and result aggregation

Takeaway: The core of Agent Team is not “the strongest agent,” but better coordination—how you organize the team, assign tasks, and move the process forward.

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Usage guide

2.1 When to use Agent Team

When is Agent Team a good fit?

Agent Team fits these kinds of tasks:

Task trait	Good for Agent Team?	Notes
Long chain, many steps	Yes	Multiple phases that can run somewhat independently
Clear role split	Yes	Different phases need different expertise
Parallelizable	Yes	Some work can run concurrently
Needs iteration	Yes	Multiple rounds of refinement
Simple Q&A	No	One agent is enough
Single-step task	No	No real need for teamwork

Typical scenarios:

1. Deep research → report

   • Research agent gathers information
   • Analysis agent processes data
   • Writing agent drafts the report
   • Proofreading agent polishes content

2. Materials → proposal

   • Organize agent sorts materials
   • Analysis agent extracts key points
   • Design agent shapes the proposal
   • Review agent refines the plan

3. Multi-role content production

   • Creative agent proposes ideas
   • Writing agent drafts content
   • Editing agent improves layout
   • Review agent checks quality

4. Complex split execution

   • Planning agent breaks down work
   • Execution agents run tasks in parallel
   • Merge agent integrates results
   • Validation agent checks completeness

2.2 Collaboration flow

Core collaboration path:

User states goal → Leader analyzes → Leader forms team → Leader breaks down tasks
→ Teammates claim tasks → Teammates execute → Teammates report back
→ Leader consolidates → Final deliverable

Step by step:

Step 1: User states the goal

The user describes the objective in natural language, for example:

• “Research the new energy vehicle industry and produce an analysis report.”
• “Organize these technical docs into a user guide.”

Step 2: Leader analyzes requirements

After receiving the goal, the Leader agent:

• Understands the core need
• Maps phases of work
• Estimates roles required
• Drafts an overall execution plan

Step 3: Leader forms the team

The Leader:

• Selects agent roles (e.g. research, analysis, writing)
• Assigns work per role
• Sets dependencies between tasks

Step 4: Teammates claim tasks

Teammates:

• Claim tasks that match their strengths
• Confirm scope, requirements, and inputs
• Prepare resources and start execution

Step 5: Teammates execute

Teammates:

• Complete work per task specification
• Escalate to the Leader when blocked
• Produce intermediate results or deliverables

Step 6: Teammates report results

When tasks finish, teammates:

• Report outcomes to the Leader
• Submit intermediate artifacts or deliverables
• Wait for follow-up assignments

Step 7: Leader consolidates results

The Leader:

• Integrates outputs from all teammates
• Checks task completion
• Produces the final deliverable

Important: This is team collaboration, not the user manually invoking agents one by one. You only state the goal; the team runs the workflow automatically.

Task dependencies:

Tasks can depend on each other:

• Prerequisite — must finish before dependents can run
• Dependent — waits on prerequisite outputs
• Parallel — no blocking dependency; can run concurrently

Example for “research → report”:

• Research (prerequisite) → Analysis (dependent) → Writing (dependent)
• Layout may run in parallel with parts of writing
• Proofreading waits until drafting completes

2.3 Starting Agent Team mode

Agent Team mode is a dedicated collaboration mode on JiuwenSwarm. You can start it as follows:

Method 1: Switch from the chat UI

On the JiuwenSwarm chat page, switch to cluster mode (Agent Team mode). This is the simplest path—pick the mode in the conversation UI.

After you click Cluster mode, the UI switches to Agent Team mode:

Method 2: /mode in a channel

In a channel conversation, run:

/mode team

The current thread enters Agent Team mode, and the Leader agent coordinates tasks.

How to write prompts in Agent Team mode

Prompting is similar to normal chat, but for smoother runs:

1. State the goal clearly — describe the final deliverable, e.g. “Research the new energy vehicle industry and produce an analysis report.”
2. Define scope — boundaries such as “focus on the domestic market, last three years.”
3. Add constraints — format, length, tone, e.g. “include charts; at least 5,000 words.”
4. Prefer one complete message — put the full request in one prompt when possible to avoid repeated replanning.

2.4 Team Skills

In Agent Team mode you can still use and develop Skills. Each agent can configure and use skills; the team can also share skill resources.

Team Skills concepts:

• Personal skills — configured and used by each agent, stored under workspaces/<agent_name>_workspace/skills/
• Team-shared skills — team-level skills under team-workspace/skills/, available to all members

How skills help the team:

1. Stronger expertise — skills extend what each agent can do in its domain
2. Shared tooling — one shared skill set avoids duplicate setup
3. Higher efficiency — skills help agents finish assigned work faster

For detailed usage and development of Team Skills, see Team Skill developer guide.

2.5 Team Memory

Each Agent Team has two memory layers: personal memory (each member reads/writes its own) and a shared TEAM_MEMORY.md (read-only to members; the Leader writes it via an extractor agent at the end of each round).

• Temporary team — members read-only access the parent agent's workspace memory; nothing persists once the team is torn down
• Persistent team — each member has isolated personal memory plus team memory that accumulates across rounds; predefined members keep their existing workspace via symlink

For the full layout, extraction categories ([decision] / [lesson] / [member] / [context]), and cross-team / cross-member isolation, see Memory → Agent Team Memory.

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Case study

Goal

Deep research on the new energy vehicle industry and deliver an analysis report.

User input

Research the new energy vehicle industry—development status, major vendors, and technology trends—and produce an analysis report.

Collaboration process

Leader analyzes requirements

After receiving the goal, the Leader:

• Phases: industry overview, competitive landscape, technology trends, market outlook, integration and writing
• Roles: industry analyst, competitive analyst, technology analyst, market analyst, writing lead
• Plan: research first, then analysis, then writing

Leader forms the team

The Leader assigns roles, for example:

• Industry analyst — industry overview
• Competitive analyst — OEM competitive landscape
• Technology analyst — technology trends
• Market analyst — market outlook
• Writing lead — integrate and write the report

Team Agent executes

After assignment, team agents execute their tasks.

Leader summarizes results

The Leader merges all outputs:

• Checks completion
• Integrates the final report
• Delivers results to the user

Output

Final deliverable: a complete new energy vehicle industry report including:

• Industry development status
• Major player analysis
• Technology trend summary
• Charts and figures as produced

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Collaboration model

3.1 Roles

Leader Agent responsibilities

The Leader is the team lead. Main duties:

Responsibility	Description
Goal understanding	Interpret the user’s objective and core needs
Team formation	Assemble the right agent team for the task
Task planning	Decompose work and assign teammates
Key decisions	Approve important choices and coordinate execution
Overall progress	Monitor progress and keep the team moving
Consolidation	Merge teammate outputs into the final artifact

Teammate Agent responsibilities

Teammates are executors. Main duties:

Responsibility	Description
Claim tasks	Take work that fits their strengths
Execute independently	Deliver per task specification
Escalate	Ask the Leader when blocked
Report results	Report completion and outcomes to the Leader
Submit artifacts	Hand off intermediates for downstream steps

Tiered autonomy

Agent Team uses tiered autonomous collaboration:

• Not fully manual orchestration — users need not assign every sub-task
• Not unmanaged — the Leader coordinates so execution stays orderly
• Autonomous claiming — teammates can pick suitable tasks and apply their strengths
• Joint advancement — Leader and teammates push the work forward together

Summary: The Leader coordinates; teammates execute; both sides complete the task together.

3.2 Shared collaboration

Team shared workspace

Agent Team provides a shared workspace where members collaborate on the same intermediate artifacts.

Typical shared content:

• Research outputs
• Analysis data
• Report drafts
• Intermediate documents
• Task status

Why sharing helps:

1. Continuous flow — research, analysis, drafts, and other artifacts move through the team
2. Less duplication — agents reuse prior outputs instead of redoing work
3. Live reference — later steps can immediately use earlier outputs
4. Joint refinement — multiple agents can improve the same document

Shared workspace layout

Agent Team storage has two layers: team shared workspace and per-member workspaces.

• Team shared workspace (team-workspace) — shared by all members; holds team artifacts and shared skills. Each Agent Team session gets its own folder containing team-workspace.
• Per-member workspaces (workspaces) — private space per agent for config, memory, skills, and todos.

Full path layout:

.agent_teams/                          ← Agent Team root
└── <team_name>/                       ← one folder per team
    ├── team-workspace/                ← shared by all members
    │   ├── artifacts/                 ← team outputs
    │   │   ├── code/                  ← code artifacts
    │   │   ├── docs/                  ← document artifacts
    │   │   └── reports/               ← report artifacts
    │   └── skills/                    ← team-shared skills
    ├── team-memory/                   ← shared team memory (auto-extracted by leader)
    │   └── TEAM_MEMORY.md
    └── workspaces/                    ← per-member workspaces
        └── <agent_name>_workspace/    ← each agent’s private space
            ├── AGENT.md               ← agent configuration
            ├── memory/                ← personal long-term memory (general scenario)
            ├── coding_memory/         ← personal coding memory (coding scenario)
            ├── skills/                ← skill library
            ├── todo/                  ← todos
            └── ...                    ← other agent-specific files

Collaboration mechanisms:

• Shared directory — one team directory holds intermediate artifacts
• Conflict control — reduces clashes when multiple agents edit the same file
• Version management — tracks changes to intermediates for traceability

UX: Users can view shared intermediates and follow progress without manually passing files.

3.3 Task progression

Event-driven execution

Agent Team does not stop after a one-time assignment; it advances through events:

How progression works:

1. Task status changes — completing a task can automatically unlock the next
2. Messaging — agents coordinate via messages
3. Recovery — the team can recover and adjust when exceptions occur

What users can observe:

1. Automatic advancement — after prerequisites finish, downstream tasks start
2. Leader approval for key decisions — important choices may require Leader sign-off
3. Traceable team state — users can follow task progress, member status, and consolidated results

Example chain for “research → report”:

• Research completes → analysis starts automatically
• Analysis completes → writing starts automatically
• Writing completes → proofreading starts automatically
• Proofreading completes → Leader summarizes results

Takeaway: Agent Team keeps running as a team—it does not end after a single assignment. Tasks flow automatically; collaboration continues until the final goal is reached.

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FAQ

Q1: Agent Team vs normal chat?

Normal chat: one agent interacting with the user—good for simple Q&A and single-step tasks.

Agent Team: multiple agents collaborating—good for complex, multi-phase work.

Q2: How do I know if I need Agent Team?

Ask:

• Does the task need multiple phases?
• Do different phases need different expertise?
• Can parts run in parallel?
• Does it need multiple refinement rounds?

If the answers are mostly yes, Agent Team is a good fit.

Q3: Leader vs Teammate?

Leader Agent: coordination—goal understanding, team formation, task planning, key decisions, overall progress, consolidation.

Teammate Agent: execution—claim tasks, execute independently, escalate, report results, submit artifacts.

Q4: How does Agent Team keep tasks moving?

Agent Team uses event-driven mechanics:

• Task status changes automatically trigger follow-on tasks
• Agents coordinate via messaging
• Exceptions can be recovered and adjusted automatically

Q5: Can users see collaboration?

Yes. Users can observe:

• Task progress
• Agent member status
• Intermediate artifacts and consolidated results

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Appendix

Related resources

• openJiuwen tech blog: Agent Team

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Simplified Chinese: Agent Team