Project Team: Single Agent vs. Multi-Agent

Enterprise-grade universal agent platform supporting hybrid task orchestration for single and multi-agent collaboration.

What is the XpertAI Project?

XpertAI Project is a customizable enterprise universal agent project space, orchestrating multiple capability units, including:

• 📎 Attachments (data/file input)
• 🛠 Custom tools (API, scripts, browser, etc.)
• 📚 Knowledge base (structured/unstructured knowledge)
• 👤 Digital expert agents (specialized skill-based agents)

The universal agent behind the project has task decision-making capabilities (exploration or planning mode), dynamically invoking resources to complete tasks autonomously or collaboratively.

Insights from Debate

Background: Two Mainstream Views on Multi-Agent Systems

Two recent articles highlight industry perspectives on multi-agent systems:

• Anthropic’s “How we built our multi-agent research system”: Emphasizes multi-agent systems for “low-dependency, parallelizable” tasks, showcasing efficiency in complex research tasks.
• Cognition (Devin AI’s parent company) “Don’t Build Multi-Agents”: Argues that in highly coupled, context-heavy scenarios (e.g., AI programming), multi-agents create collaboration and context management issues, advocating for single-agent context consistency.

These seemingly opposing views reflect rational choices for different task contexts. Beyond single-task solutions, agent reusability is key for enterprise platforms. XpertAI supports both architectures, focusing on reusable “digital experts” as enterprise knowledge assets, configurable across projects and workflows.

Mode Analysis: Multi-Agent vs. Single-Agent

Mode 1: Multi-Agent Collaboration (Parallel Tasks)

Multiple specialized “digital expert agents” coordinated by a universal agent.

📌 Use Cases:

• Market research, competitor analysis, batch audits
• Multi-document integration, large-scale knowledge retrieval
• Low-dependency, concurrent subtasks

✅ Design Principles:

• Main agent (universal) decomposes tasks, coordinates workflows
• Sub-agents execute specific subtasks with independent contexts
• Structured output, main agent aggregates results

⚙️ Configuration:

• Set up multiple “digital expert” agents (e.g., web search, summarization, comparison experts)
• Define clear goals and formats for subtasks to ensure stable outputs
• Main agent uses control prompts for collaborative integration

⚠️ Risks:

• Sub-agents can’t share context, limiting applicability
• Task dependencies require main agent to manage context

🔎 Example: “Analyze strategic plans, revenue, and trends of 10 energy companies.” → Main agent splits tasks → Sub-agents research each company → Main agent compiles structured report.

Collaboration among experts is coordinated by the universal agent without human intervention.

Reference Template: Deep Research Project (Multi-Agent)

Mode 2: Single-Agent Execution (Sequential Tasks)

Universal agent autonomously plans and executes tasks using all resources.

📌 Use Cases:

• AI programming, script generation, financial reporting
• Contract drafting, legal reviews, process rule creation
• Long task chains, high context coupling

✅ Design Principles:

• Maintain complete context to avoid fragmentation
• Step-by-step task progression to manage token pressure
• Use summarization to maintain information continuity

⚙️ Configuration:

• Single universal agent with tools (e.g., code executor, document analyzer)
• Prompts control task steps (e.g., “Explain, then provide code”)
• Intermediate interactions for error correction

⚠️ Risks:

• No concurrency, longer execution for long tasks
• Token overflow risk, mitigated by summarization and step-wise strategies

🔎 Example: “Generate a Python script for sales data analysis with a trend chart.” → Agent explains logic → Writes code → User confirms → Generates script → Renders chart.

No need to orchestrate workflows or specify tools; the agent autonomously completes the process.

Reference Template: Deep Research Project (Single-Agent)

Choosing a Mode: Task Characteristics and Reusability

Feature	Multi-Agent Mode	Single-Agent Mode
Parallelization	Strong, suits task decomposition	Weak, suits sequential tasks
Context Sharing	None, main agent coordinates	Full context retention
Controllability	Moderate, needs prompt design	High, traceable flow
Suitable Tasks	Multi-source integration, research	Programming, writing, audits
Agent Reusability	High, modular expert components	Moderate, task-specific
Risk Control	Context fragmentation, inconsistency	Token pressure, efficiency

In XpertAI, both modes are flexibly selected via project creation. Multi-agent for information integration; single-agent for context-heavy tasks. Agents are configured as reusable “digital experts,” forming an enterprise “smart capability library.”

How to Configure a Project

Step 1: Create Project

Click “New Project” on the “Conversation” homepage and name it.

Step 2: Add Resources

In the “Tools” tab:

• ✅ Tools (e.g., bash, browser, custom APIs for system integration)
• ✅ Attachments: Upload spreadsheets, contracts, blueprints
• ✅ Knowledge Base: Add manuals, standards, past cases
• ✅ Digital Experts: Select or create experts via “Add Expert”

Step 3: Engage Project Team

Switch to “Exploration Mode” to issue commands; the agent auto-calls resources. In “Planning Mode,” the agent outlines task steps before execution.

Case Studies

Scenario	Mode	Capabilities Involved
Monthly Sales Report	Single-Agent	File tools, knowledge base, charting
Job Description Writing	Multi-Agent	Job expert, language optimizer, norms
Budget Forecasting	Single-Agent	Excel, budget rules, prediction tools
Competitor Analysis	Multi-Agent	Search, summarization, visualization experts

Comparison with Other Products

Feature	XpertAI	Coze Space	ManusAI	SunaAI
Multi-Agent Orchestration	✅ Expert collaboration	❌	Partial	❌
Custom Tool Integration	✅ Highly extensible	✅	❌	❌
Enterprise Knowledge Integration	✅ Full support	✅	✅	❌
File Comprehension	✅ Strong	Average	Strong	Moderate
Ease of Use	Low barrier	High	Moderate	Moderate

Summary

Multi-agent isn’t a cure-all; single-agent isn’t outdated. Success lies in choosing the right mode for the task and structuring the system effectively. XpertAI supports both modes, enhancing enterprise-grade agent reusability. Understand their boundaries to build flexible, reliable, and scalable agent systems.

👉 Start your XpertAI project to empower your business!