TLDR: The best coding agents are simple `while(tool_use)` loops with exceptional context engineering. Multi-agent systems introduce coordination overhead, cascading failures, and state synchronization problems. Start simple. Master context engineering first. Add coordination only when you've exhausted single-agent optimization.
This is part 2 of a series on context engineering. Start with Signal Is the Bottleneck.
The Complexity Trap
Remember when everyone rushed to containerize everything? Kubernetes for a three-service app. Microservices for a monolith that worked fine. We're seeing the same pattern with multi-agent systems. "Agentic frameworks" are everywhere. The promise? Orchestrate dozens of specialized agents, each handling a specific task, coordinating to solve complex problems everything.
The reality? The best coding agents today are simple while(tool_use) loops with exceptional context engineering. Claude Code, Codex, Cline, Amp Code. They win on context, not coordination. Their engineering decisions are context-first. Every design choice optimizes for what goes into the window and when. Architecture, retrieval strategy, caching, all built around maximizing signal per token.
Multi-agent systems introduce real complexity. Coordination overhead, cascading failures, state synchronization. Most problems don't need this.
When Multi-Agents Actually Help
When do multi-agents actually help? When the problem is genuinely decomposable and the coordination cost is justified.
What's the difference between multi-agent orchestration and sub-agent architectures? Multi-agent systems coordinate multiple independent agents that run simultaneously, sharing state and communicating through complex protocols. Sub-agent architectures spawn isolated forks of the main agent for specific tasks.
Why does this matter for coding agents? Sub-agents are frequently used to manage context windows. A fork of the main agent performs a task in isolation, then provides a clean summary back to the main agent. The context window stays pristine. No accumulated state, no coordination overhead.
- A sub-agent for context management? Sure.
- A fleet of 10 agents for a code change? Overkill.
The Coordination Tax
Multi-agent systems don't solve context engineering. They multiply it. Each agent still needs signal extraction. Now you also need agent-to-agent communication protocols, shared state management, conflict resolution, and orchestration logic. With single agents or sub-agents, the only lever is context engineering. With multi-agents, you add coordination overhead, message passing complexity, and distributed state problems on top of the context engineering you still have to solve.
Context engineering scales linearly. Better retrieval, better signal extraction, better results. Multi-agent systems scale linearly exponentially. More agents, exponentially more interaction patterns to manage. Start simple. Master context engineering first. Add coordination only when you've exhausted single-agent optimization.
The Winning Pattern
The agents that win won't be the ones with fancier architectures. They'll be the ones that master extracting signal from context. They'll load less but understand more. They'll know when to front-load, when to lazy-load, and when to prune ruthlessly.
Context engineering scales linearly while architectural complexity explodes exponentially. Better retrieval, better ranking, better pruning. These are the levers that maximize signal and multiply agent effectiveness.
Signal isn't optional. It's the driving factor that determines whether AI agents fail or succeed. Every hallucination, every wrong turn, every wasted token traces back to poor signal extraction through context engineering.
Start simple. Master signal first.
What's Next
Simple architectures win when context engineering is done right. But how do you actually maximize signal?
The next part explores the mechanics of signal extraction. How to identify what matters, surface it at the right time, and cut what doesn't. The technical implementation of turning noise into signal.