Agents And Tool Use medium
Your team split a single coding agent into planner, coder, and reviewer agents, hoping for better reasoning. Quality is unchanged, cost tripled, and handoffs keep losing details. In which situation is a multi-agent split actually justified?
- A Whenever the task is complex, because dividing roles across agents lets each one reason more intelligently than a single agent.
- B Whenever more total tools are needed, because adding agents that each carry more tools makes the overall system smarter.
- C Whenever you need reliability, because agents checking each other's work removes the need for external approval gates.
- D When subtasks are independent and can run in parallel, or when isolating a noisy subtask's context keeps the main one clean.
Answer: D. When subtasks are independent and can run in parallel, or when isolating a noisy subtask's context keeps the main one clean.
Multi-agent splits pay off through **parallelism and context isolation**, not added intelligence. The same model in three roles reasons no better, and every handoff can lose information. Option A is the tempting misconception. The tradeoff is extra cost and handoff loss against a cleaner context and lower wall-clock time, so keep a single agent when the subtasks share most of their context.
One system design question a day, with spaced repetition so the ones you miss come back.
Get the appRelated questions
- You are defining a 'schedule_meeting' tool for an agent. Which schema decision most reduces wrong or malformed calls?
- A model emits three parallel tool calls: `get_inventory`, `get_price`, and `get_shipping`. To cut latency, the host appends each result to the transcript as it arrives and immediately re-invokes the model. Runs start failing with malformed-request errors, and some produce hallucinated shipping costs. What should the host do?
- A team splits a coding agent into planner, coder, tester, and reviewer agents, and each receives the full transcript of the previous agent. The expected quality gains did not appear. Cost tripled, and the reviewer often fixates on approaches that earlier agents abandoned. When is a multi-agent split actually justified?
- A support agent should remember each customer's preferences across months of sessions. The team appends every past session summary to the system prompt. After six months, prompts exceed 80,000 tokens, latency has doubled, and the agent sometimes ships to an address the customer replaced weeks ago. What redesign fixes both problems?
- A teammate proposes splitting a single coding agent into five agents, arguing that more agents means smarter results. When is a multi-agent split actually justified?
- A data-analysis agent runs model-generated Python inside the same container as your API service. That container has database admin credentials in environment variables. A security review flags this as critical. What is the best mitigation?