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You can build a multi-agent AI system without CrewAI or AutoGen by coordinating agents with ordinary application code and an agent SDK or framework. Start with one agent, add specialists only when a task genuinely needs different instructions, tools, or policies, and decide up front whether the orchestrator or a specialist owns the final response. Use MCP to connect agents to tools and resources; use A2A when independent agents need to communicate or delegate across service boundaries.
Decide whether you need multiple agents
Multiple agents are an orchestration choice, not a prerequisite for an agent application. OpenAI’s official Orchestration and handoffs documentation advises: “Start with one agent whenever you can.” A specialist adds value when a branch needs a distinct role, tool set, or policy—not simply because a workflow has several steps.
Before splitting a workflow, write down the outcome the user needs, the information each step may access, the tools it may call, and the output each step must produce. Keep fixed rules and predictable decisions in application code; reserve model reasoning for decisions that actually benefit from it. This helps keep control flow explicit instead of asking a model to route every action.
Choose who controls the response
Keep the manager in charge with specialists as tools
The manager calls a specialist for bounded work, receives its result, and remains responsible for assembling the user-facing answer. This fits subtasks such as classification, summarization, or research when one agent should synthesize the result.
Hand off when a specialist should take over
A router or triage agent can transfer control to a specialist that should handle the selected branch directly. Make the specialist’s remit and the handoff description specific so the routing decision is understandable and the specialist knows what it is expected to do.
Pick an orchestration pattern
Code-directed sequence
Application code can run a known sequence—such as research, drafting, critique, and revision—and pass each step’s output to the next. It can also run independent steps in parallel or repeat an evaluator-and-revision loop. Use a loop only when there is a defined condition for stopping; otherwise, the workflow can continue without a clear reason to finish.
Model-directed routing
A model can choose the next step when the right route depends on the task and benefits from dynamic planning. This is more flexible, but gives the model more responsibility for control flow. The two approaches can be mixed: keep the outer workflow in code and allow a model to choose among a limited set of appropriate actions.
Do not run dependent work concurrently: a step that needs an earlier result must wait for it. Parallelize only tasks that are independent. OpenAI’s SDK documentation describes code-directed orchestration as an option for chaining, evaluator loops, and independent parallel work; these are patterns, not a claim that multiple agents automatically improve results.
Connect tools and agents at the right boundary
Use MCP for tools and resources
The Model Context Protocol (MCP) connects an agent to tools, APIs, and other resources. It addresses the agent-to-tool boundary; it is not a replacement for deciding how your application routes work between agents.
Use A2A for independent agents
Agent2Agent (A2A) supports communication and task delegation between independent agents. It is useful when an agent is deployed as its own service or needs to collaborate across frameworks or organizational boundaries. A2A documentation describes it as complementary to MCP—not an agent-development kit or a replacement for MCP.
Rank #4
Choose local or remote specialists
An in-process specialist is tightly coupled to its orchestrator and avoids network latency and protocol-serialization overhead described in Google’s ADK example. A remote A2A agent has an independent service boundary, which can suit separate deployment or cross-framework collaboration, but adds network communication and protocol handling.
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare the main design choices
| Decision | Choose the first option when | Choose the second option when |
|---|---|---|
| Model-directed or code-directed | Dynamic planning or routing is useful. | A fixed sequence, explicit control, or predictable behavior matters. |
| Specialist as tool or handoff | The manager should retain ownership of the final answer and use specialists for bounded work. | A specialist should take over and handle the routed branch directly. |
| Local subagent or remote A2A agent | The specialist belongs inside one orchestrator and low communication overhead matters. | The specialist needs an independent service boundary or cross-framework communication. |
These are documented design patterns, not a ranking of frameworks or a promise that one option will perform better in every application.
Make the workflow observable and safe to change
- Give each specialist a narrow responsibility and define the input and output it is expected to handle.
- Use clear, specific routing descriptions so the orchestrator can distinguish when each specialist is appropriate.
- Validate model outputs in code before using them to select a consequential next step; structured outputs can help make that boundary explicit.
- Monitor traces and evaluate task outcomes as you change prompts, routes, or agent responsibilities.
- Set application-level limits and error handling for retries, timeouts, and actions with side effects. The sources describe the need for operational care but establish no universal numeric limits.
Additional agents mean additional prompts, traces, and approval surfaces. Keep the system as simple as the user outcome permits, and add complexity only when evaluation shows the workflow needs it.
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