Teams start with the wrong multi-agent pattern, skip hard limits, and underestimate orchestration complexity. That combination produces systems that look smart in a demo but fail when workload, errors, and edge cases show up.
The Consequence
- Weeks wasted building an architecture that doesn’t match the task.
- Runaway cost risk: without limits, agents can loop and burn.
- Operational pain: without structured orchestration and logging, debugging becomes “nearly impossible,” and systems remain toys instead of reliable services.
The core problem: A single AI agent hits a ceiling fast. It cannot research, write, fact-check, and format a report in one pass. Multi-agent systems solve this by coordinating specialised agents — but the coordination pattern you choose determines whether your system scales or collapses. And without hard limits, any of these patterns can loop indefinitely.
After synthesising 18 passages from 11 published AI/ML books covering multi-agent architecture, the coordination failures fall into three distinct pattern categories — each with different trade-offs, failure modes, and cost implications. Here are all three.
Three Coordination Patterns That Determine Everything
Every multi-agent system must answer one question: who decides what happens next? The answer falls into three patterns, and choosing the wrong one wastes weeks of development time and produces a system that doesn't match your needs.
One agent receives the user's request, breaks it into sub-tasks, assigns each sub-task to a specialised agent, and collects results. The supervisor never performs the actual work — it only plans and delegates. If the user asks for a research report, the supervisor assigns one agent to gather information, another to write the draft, and a third to fact-check.
The risk: The supervisor is a single point of failure. If it makes a bad plan, every downstream agent executes the wrong work. And without iteration limits, the supervisor can enter a replan loop — unsatisfied with results, it reassigns the same task repeatedly. Each cycle burns tokens.
Best for: Small teams of 3–7 agents with clearly separated skills.
When you have twenty specialised agents, a single supervisor cannot manage them all. Hierarchical architecture organises agents into teams with team leaders, creating management layers similar to company org charts. A top-level supervisor oversees team leaders, and each team leader supervises their own specialists.
The risk: Failures cascade through layers. A bad decision at the top-level supervisor propagates to every team. Debugging requires tracing through multiple management levels. And the cost multiplier is real — a loop at the top level triggers loops in every team below it.
Best for: Complex workflows requiring 10+ agents organised into functional teams.
No central controller. Agents communicate directly and hand off work dynamically. The research agent finishes and hands off to the writer. The writer discovers gaps and hands back to the researcher. This continues until both agents agree the work is complete.
The risk: This is where the $10,000 loop lives. Without hard limits on total handoffs, agents can pass work back and forth indefinitely — each handoff generating a full context window of tokens. Twenty-five handoffs between agents, each processing thousands of tokens, creates runaway costs that are invisible until your API bill arrives.
Best for: Dynamic tasks requiring natural back-and-forth collaboration — but only with strict handoff limits.
Four Ways Multi-Agent Systems Fail in Production
Choosing the right pattern is necessary but not sufficient. Every coordination pattern shares these failure modes — and each one can generate costs that dwarf your development budget if left unchecked.
The supervisor agent is unsatisfied with a sub-agent's work and reassigns the task. The sub-agent returns a similar result. The supervisor reassigns again. Without a maximum iteration count, this cycle never terminates. Each iteration consumes a full LLM inference call — input tokens for the context plus output tokens for the new plan.
In swarm patterns, Agent A hands to Agent B, which hands to Agent C, which hands back to Agent A. The circular dependency creates an infinite loop with no natural exit condition. The system has no global view of how many handoffs have occurred — each agent only sees its own immediate context.
Every handoff accumulates context. The research agent's output becomes the writer's input, which becomes the evaluator's input, which feeds back to the researcher. After several cycles, the context window hits its limit. The system either fails silently (truncating critical information) or switches to a larger, more expensive model automatically.
Agents calling external tools — APIs, databases, search engines — with each tool call costing time and money. A planning agent that generates five tool calls per step, across four agents, across three iterations, produces 60 tool calls from a single user request. If any tool returns an unhelpful result, the agent retries — compounding the cascade.
These four failure modes share a root cause: the absence of hard limits at every level of the architecture. The coordination pattern determines where failures occur. The limits you build determine whether they spiral.
The prevention framework — including planning systems, memory architecture, tool use safeguards, orchestration framework selection, and a prioritised 7-step implementation plan — is what stops these failures before they reach production.