Why Multi-Agent Systems Are a Trap: Lessons from Cognition AI on Building Reliable AI Agents
The multi-agent dream is seductive. Picture it: dozens of AI agents working in parallel, each tackling a piece of your problem, coming together like a well-oiled machine to deliver results. OpenAI pushes Swarm. Microsoft promotes Autogen. CrewAI promises orchestrated agent teams.
But here's the uncomfortable truth that Cognition AI (the creators of Devon) just revealed: multi-agent systems are a trap.
After hundreds of hours building AI agents and seeing what actually works in production, I can confirm their findings. The more agents you add, the more your reliability plummets. Let me show you why, and more importantly, what actually works.
The Multi-Agent Disappointment
A year ago, the AI community was buzzing with multi-agent frameworks. The promise was compelling: decompose complex tasks, distribute them across specialized agents, and watch the magic happen.
But ask yourself: how many production applications are actually using these frameworks successfully?
The answer is telling: almost none.
This perfectly matches my experience building Vectoral, an AI startup used by over 50,000 people. We spent countless hours trying to make multi-agent systems reliable. What we discovered was sobering: complexity kills reliability.
Two Principles That Change Everything
Walden Yan, co-founder of Cognition AI, distills the solution down to two critical principles:
Principle 1: Share Context
Every agent needs the full context of what came before. Not summaries. Not filtered views. The complete picture.
Principle 2: Actions Carry Implicit Decisions
When agents act on incomplete information, they make conflicting assumptions. These conflicts compound into system-wide failures.
These principles might seem constraining, but they're liberating. They eliminate entire classes of architectural failures before you write a single line of code.
The Architectures That Don't Work (And Why)
Let me show you the most common trap with actual code:
The Parallel Agent Trap
Here's what happens with a real example. Task: "Build a Flappy Bird clone"
- Sub-agent 1: Creates a dark, gritty environment with realistic physics
- Sub-agent 2: Builds a colorful, cartoonish bird with arcade physics
- Main agent: Tries to combine incompatible work
The agents made conflicting decisions because they couldn't see each other's work. The result? A mess.
The Architecture That Actually Works
Here's the breakthrough: linear, single-threaded agent chains with full context propagation.
The difference is profound. Each agent sees:
- The original task
- All previous agent decisions
- Complete reasoning traces
No conflicts. No surprises. Just reliable execution.
Context Engineering: The Real Game Changer
While everyone obsesses over prompt engineering, the real leverage is in context engineering.
Think about it: even the smartest person can't do their job without proper context. The same applies to AI agents, but even more so.
Context engineering involves:
- Structuring information for maximum clarity
- Preserving decision rationales
- Maintaining consistency across agent boundaries
- Compressing context intelligently for long-running tasks
Learning from the Best: How Devon and Claude Code Work
It's no coincidence that the most successful AI agents follow these principles:
Claude Code's Approach
- Never runs subtasks in parallel
- Sub-agents only answer specific questions
- Only the main agent writes code
- Investigative work stays out of the main conversation
Devon's Architecture
- Linear task execution
- Full context propagation
- Intelligent context compression for long tasks
- No parallel agent coordination
These aren't limitations—they're features. By constraining the architecture, they achieve reliability at scale.
The Context Compression Challenge
For truly long-running tasks, you'll hit context limits. Here's the advanced pattern:
But beware: context compression is hard. Multi-billion dollar companies struggle with it. Only add it when absolutely necessary.
Practical Recommendations for Builders
- Start Simple: Use linear, single-threaded architectures
- Share Everything: Never hide context between agents
- Avoid Parallelism: Sequential execution prevents conflicts
- Log Decisions: Make implicit reasoning explicit
- Test Reliability: Measure success rates, not just speed
The Future is Context, Not Complexity
The AI agent landscape is evolving rapidly, but not in the direction many expected. Instead of complex multi-agent orchestration, the winners are focusing on context engineering and architectural simplicity.
Remember: we're still in the HTML/CSS era of AI agents. React hasn't been invented yet. The frameworks pushing multi-agent complexity are solving the wrong problem.
The real challenge isn't coordinating multiple agents—it's giving a single agent chain the context it needs to succeed.
Your Next Steps
- Abandon multi-agent frameworks that encourage parallel execution
- Adopt linear architectures with full context propagation
- Master context engineering instead of agent orchestration
- Measure reliability as your north star metric
The companies building the most advanced AI agents in the world—Cognition, Anthropic, OpenAI—have all reached the same conclusion: simple architectures with rich context beat complex architectures every time.
Don't fall for the multi-agent trap. The future belongs to those who master context engineering.
Want to dive deeper? Check out the linear-agents GitHub repository for production-ready agent architectures you can build on. All code is MIT licensed.