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Why Multi-Agent Systems Are a Trap: Lessons from Cognition AI on Building Reliable AI Agents

•5 min read•By Brandon J. Redmond
AI EngineeringAgent ArchitectureContext EngineeringDevonBest Practices

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

  1. Start Simple: Use linear, single-threaded architectures
  2. Share Everything: Never hide context between agents
  3. Avoid Parallelism: Sequential execution prevents conflicts
  4. Log Decisions: Make implicit reasoning explicit
  5. 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

  1. Abandon multi-agent frameworks that encourage parallel execution
  2. Adopt linear architectures with full context propagation
  3. Master context engineering instead of agent orchestration
  4. 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.