A single agent handles a task in one reasoning context with its tools; a multi-agent system uses an orchestrator to delegate subtasks to specialized workers. Start single-agent — it is simpler, cheaper, and easier to evaluate — and add agents only when a task is genuinely too broad or varied for one.
By Evgeny Aleksandrov, Founder, BlackGrid ·
At a glance
Dimension
Single agent
Multi-agent
Structure
One agent + tools
Orchestrator + workers
Complexity
Lower
Higher
Token cost
Lower
Higher
Debuggability
Easier
Harder
Best for
Focused tasks
Broad, varied tasks; parallelism
Failure modes
Fewer
More (coordination overhead)
When to choose Single agent
The task fits in one reasoning context
You want lower cost and latency
You need it easy to evaluate and debug
As the default — start here
When to choose Multi-agent
The task has separable subtasks
Different steps need different tools or context
Parallel exploration adds value
The work is too broad for one context
Can you use both?
Treat it as a ladder, not a binary. Begin with a single agent and escalate to multi-agent only when evaluation shows one agent cannot cover the task. The simplest design that works is the right one.
When a task has separable subtasks, needs different tools or context per step, or benefits from parallel exploration. If one well-equipped agent does the job, that is the better system.
Are multi-agent systems more accurate?
Not inherently. They add capability for broad tasks but also cost, latency, and failure modes. Accuracy comes from evaluation and grounding, not from the number of agents.
What is the downside of multi-agent systems?
More tokens, more latency, and harder debugging. Anthropic's guidance is to find the simplest solution that works and add complexity only when it demonstrably helps.