You need to decide something big. Do you want one AI to do all the work? Or do you want many AIs to handle different tasks? This choice matters for building smart AI systems. These systems can study company data, run apps, or manage websites. The answer isn’t a simple “yes” or “no.” Instead, it depends on your goals. It also depends on how tricky the task is. And it relies on how well you control the system. In this blog, we’ll explore the debate between one AI and many AIs. We’ll look at real-world issues too. Finally, we’ll give tips for picking the best option in June 2025.
Breaking Down the Tech Jargon
Let’s make some terms clear and simple:
- AI Agent: A clever program, like a chatbot or data checker, that uses AI for specific jobs.
- MCP (Model Context Protocol): A method to link AI models to outside tools and data. It helps AI work with things like CRMs or design software.
- RAG (Retrieval-Augmented Generation): A trick that lets AI grab fresh info from the web or databases to improve its answers.
- Tools: Add-ons for AI, such as APIs for stock prices or text checkers.
- Tool Confusion: When AI gets mixed up with too many tools or wrong data, causing mistakes.
- Single-Agent Design: One AI handles everything, like a lone cook making a meal.
- Multi-Agent Design: Several AIs team up, each with its own role, like a kitchen crew with different skills.
Imagine an AI as an office worker. One AI might manage emails, spreadsheets, and meetings alone. But that can be overwhelming. A multi-agent setup splits the tasks among experts. So, which is better? Let’s find out.
The Single-Agent Approach: Simplicity Wins (Sometimes)
Suppose you build an AI to track Tesla. It needs to share news, stock prices, and social media vibes. A single-agent design puts all this in one AI’s hands. Here’s what it uses:
- RAG to fetch news articles.
- MCP to tap into tools like a stock API.
- Tools to check feelings on social media.
This works well for straightforward jobs. For example, the Cognition AI team, creators of the Devin tool, wrote a blog on June 12, 2025. They said one AI is more dependable. Multi-agent setups often mess up because they don’t share info well or they clash. Instead, they recommend one AI with strong info control, like Claude Code’s long-trace system, for tasks with many steps.
Good Things:
- It’s easier to handle and fix.
- There’s less chance of disagreements.
- It suits tasks that focus and flow in order.
Bad Things:
- It struggles with varied or side-by-side tasks.
- It can fail when things get too complicated.
The image you shared shows a single-agent system splitting a task into smaller bits. It uses context crunching (like with LLMs) to stay steady on long jobs. This proves a smart single AI can tackle tough stuff if it handles info right.
The Multi-Agent Approach: Teamwork Makes the Dream Work (With Caveats)
Now, picture a multi-agent design for the Tesla project. Each AI takes a role:
- Research Agent: Uses RAG and text tools for news.
- Financial Agent: Tracks stocks with an API.
- Sentiment Agent: Checks social media buzz.
Each AI focuses on one thing and uses MCP to reach tools. Jason Zhou posted on X on June 13, 2025, backing this idea. He said sub-agents can manage small tasks if they share stuff like chat history. However, he warned about “merge conflicts” if tasks overlap without good planning. Cognition AI mentions this problem too.
Good Things:
- Each AI is a pro, so answers are sharper.
- You can grow it for big, messy tasks.
- It’s like people teaming up for huge projects.
Bad Things:
- Without good info sharing, it gets messy.
- Today’s tech makes it tough to sync up.
- It breaks easily if not built just right.
Why It’s Case by Case
No single way wins every time—it depends on the situation:
- Simple Tasks: For stuff like daily news summaries, one AI with RAG and MCP is enough. This fits Cognition AI’s love for easy solutions.
- Complex Tasks: For a full Tesla breakdown (news, stocks, vibes), many AIs shine if they share info via MCP and stay in sync, as Jason Zhou points out.
- Tech Today: In June 2025, one AI is safer since multi-AI setups can be shaky. But tools like MCP might change that soon.
Think of making an app. A calculator app needs one piece. But an online shop needs parts for listings, payments, and shipping—each with its own crew. AI design works the same way.
Can One Agent Handle Multiple Tools?
Yes, one AI can use tools like RAG and MCP if they match its purpose. For instance, a Research Agent might use RAG for news and MCP for extra tools. But toss in a stock API, and it might stumble—like asking a writer to do math homework. Many AIs keep tools clear and focused.
The Verdict: Choose Wisely
This debate isn’t about crowning a winner. It’s about picking what fits the job. One AI rocks for simple, steady tasks, as Cognition AI pushes. Many AIs flex for big, team-based jobs, but only with solid info sharing, as Jason Zhou and Anthropic’s studies suggest. MCP boosts both by making tools standard.
What to Do:
- Check your needs: Pick one AI for step-by-step tasks, or many for tasks at once.
- Use MCP: Keep tools and info easy to reach and consistent.
- Watch 2025 tech: As info sharing gets better, multi-AI setups might take off.
In June 2025, AI keeps changing fast. Staying flexible and choosing smart are key. Start small, grow wisely, and adjust as tech improves.
Sources
- X Post by Jason Zhou, provides insights on multi-agent challenges and context management.
- Cognition AI Blog: “Don’t Build Multi-Agents”, explains why single-agent systems are more reliable.
- Anthropic – “Introducing the Model Context Protocol”, defines MCP accurately for AI integration.
- Anthropic – “How we built our multi-agent research system”, explores successful multi-agent designs.
- Addy Osmani’s Blog: “MCP: What It Is and Why It Matters”, explains MCP in an accessible way.
- Wikipedia – Model Context Protocol, verifies MCP as an industry standard.
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