A practical field guide from Automation Ace.
Short answer: one AI agent is enough for most business tasks: a single agent with a clear prompt and a focused set of tools is cheaper, faster, and easier to debug. Add subagents when one agent is clearly struggling: it has too many overlapping tools, conflicting instructions for different kinds of work, more context than it can hold, independent subtasks that could run in parallel, or parts that need very different permissions. Before adding agents, try better tools, a tighter prompt, or a plain workflow that chains steps.
Short answer
- Default: one agent, a focused toolset, good instructions.
- Next: a workflow that chains AI steps in a fixed order.
- Then: a router or coordinator with specialist subagents.
- Add agents for separation of tools, context, permissions, or parallel work; not because it sounds advanced.
Start with one agent
Most agent problems that look like “we need more agents” are really unclear instructions, vague tool descriptions, or missing data. A single agent is easier to test and monitor, and every handoff you add is a place where context can be lost. Get one agent working well first; see how to build your first AI agent.
Signals you need more than one
| Signal | One agent is enough | Consider subagents |
|---|---|---|
| Number of tools | Up to roughly 10 to 15 distinct tools | Many overlapping tools; the agent keeps picking the wrong one |
| Instructions | Fit on a page or two | Conflicting rules for different kinds of work |
| Context | Fits comfortably in one run | Research or documents overflow the context |
| Subtasks | Sequential and related | Independent and can run in parallel |
| Permissions | Same access for the whole job | Parts need very different access levels |
| Expertise | One domain | Clearly separate specialties (legal, finance, code) |
| Volume and cost | Cost matters more than speed | Parallel speed is worth extra usage |
The tool count is a rough guide, not a rule; it depends on how distinct the tools are. Overlap matters more than the number. See how agents use tools.
Multi-agent patterns
| Pattern | How it works | Good for |
|---|---|---|
| Single agent with tools | One agent, one prompt, a toolset | Most first agents and most business tasks |
| Workflow chain | A workflow calls AI steps or agents in a fixed order | Known stages: extract, then classify, then draft |
| Router | A first step classifies the request and sends it to the right specialist | Support or inbox with distinct request types |
| Coordinator with subagents | A lead agent plans, delegates subtasks, and combines results | Research, large reviews, multi-part projects |
| Handoff | One agent passes the conversation to another specialist | Sales to support, triage to billing |
| Reviewer | A second agent checks the first agent's output against rules | High-stakes drafts and policy checks |
Notice that the first two patterns are not multi-agent at all. A fixed chain of steps is a workflow, and workflows are often the right answer; see AI agents vs automations.
What subagents cost you
| Cost of adding agents | Why it matters |
|---|---|
| More model usage | Each subagent has its own context and calls; multi-agent runs can use several times the tokens |
| More latency | Coordination adds steps, unless subtasks run in parallel |
| Lost context between agents | Handoffs drop details unless you pass them explicitly |
| Harder debugging | Failures can hide in a handoff or a summary |
| More to test and maintain | Each agent has its own prompt, tools, and test set |
For budgeting, see what it costs to run an AI agent.
How to split an agent well
- Split by job, not by step: “researcher” and “writer,” not “step 1 agent” and “step 2 agent.”
- Give each subagent only its tools and the access its job needs.
- Define handoff contracts: what goes in, what comes back, in structured form.
- Return summaries, not transcripts, so the coordinator's context stays small.
- Keep one owner of the final output and one place where approvals happen.
- Trace the whole run across agents so you can find where it went wrong; see testing and monitoring agents.
Example: from one agent to three
A company starts with one inbox agent that triages email, answers billing questions, and handles sales inquiries. Over time it has 25 tools, rules for billing that conflict with rules for sales, and billing needs access sales should not have. They split it:
- Router: classifies each email and sends it on. No write tools.
- Billing agent: order and invoice tools; refunds need approval.
- Sales agent: CRM and calendar tools; drafts replies for a rep.
Each has a short prompt, a small toolset, and its own tests. A workflow logs every handoff. Permissions follow the job, as described in permissions, approvals, and audit trails.
Subagents on popular platforms
- Make: see coordinating multiple AI agents and subagents in Make.
- Agent SDKs: the OpenAI Agents SDK supports handoffs between agents, the Claude Agent SDK supports subagents, and Google's Agent Development Kit (ADK) supports multi-agent hierarchies.
- AI employees: Lindy and similar tools let agents hand work to each other; see how to build AI employees.
- Hosted teams of bots: Grok Bot is built around named bots that hand off work; see the four personal agents compared.
For a platform overview, see where to build AI agents.
Frequently asked questions
When is one AI agent enough?
For most business tasks. If the agent has a focused set of distinct tools, instructions that fit on a page or two, context that fits in one run, and one level of access for the whole job, a single agent is cheaper, faster, and easier to debug.
When should I add subagents to an AI agent?
When the agent has many overlapping tools it keeps confusing, conflicting instructions for different kinds of work, more context than it can hold, independent subtasks that could run in parallel, or parts of the job that need very different permissions.
What is the difference between an agent and a subagent?
A subagent is an agent that another agent, usually a coordinator, delegates a specific subtask to. It has its own instructions, tools, and context, and returns a result to the coordinator.
Are multi-agent systems more expensive?
Usually. Each agent has its own context and model calls, so multi-agent runs can use several times the tokens of a single agent, and handoffs add latency unless subtasks run in parallel.
How do I split an AI agent into subagents?
Split by job rather than by step, give each subagent only the tools and access it needs, define structured handoff contracts, return summaries instead of transcripts, keep one owner of the final output, and trace runs across agents.
Disclaimer: AI agent platforms, product names, features, and prices change quickly; details reflect public documentation and coverage at the time of writing, so confirm current details with each provider before building. Automation Ace is not affiliated with the companies named here. This article may include links to apps, products, or services; some links may be affiliate links, which means Automation Ace may earn a commission at no extra cost to you.