Orchestration Topologies: How Agents Coordinate Workflows

Idea in 30 seconds

Orchestration Topologies is a way to organize interaction between multiple agents in one system.

This is not about "smarter prompts" but about structure: who receives the task, who executes subtasks, and how results return.

When needed: when one agent can no longer handle the task alone and multiple roles or agent teams are required.

The LLM can suggest a plan, but the orchestration topology defines the safe and predictable execution path.

Problem

When multiple agents appear in a system without a clear topology, the process quickly becomes chaotic.

Typical problems:

• it is unclear which agent is responsible for what;
• subtasks are duplicated or lost;
• agents "bounce" the task between each other without completion;
• it is hard to control stop conditions and global limits across the entire system;
• it is hard to localize an error because there is no explicit execution path.

As a result, the system spends more resources, while quality and predictability drop.

Solution

Add Orchestration Topology as an explicit coordination scheme for a specific multi-agent system: who orchestrates, who executes, how handoffs happen, and when everything stops.

This gives a transparent route for the task from start to final result.

Analogy: like a public transport network.

A passenger can take different routes, but there are clear hubs, transfers, and a final station.

Orchestration Topology defines the route for the task between agents in the same way.

How Orchestration Topologies work

Orchestration Topology is an explicit coordination scheme between a coordinator and an agent team, defining the order of subtask handoff and result aggregation.

Runtime operates inside each agent, so it is not shown as a separate node in this diagram.

This cycle provides a controlled task path between agents and reduces chaos in a multi-agent system.

Typical Orchestration Topologies

Quick selection guide:

• Centralized - when you need simple and transparent control through one coordinator.
• Pipeline - when each next step depends on the previous one.
• Parallel - when subtasks are independent and speed matters through concurrent execution.
• Hierarchical - when there are multiple coordination levels (team/subteam direction).
• Hybrid - when one system needs different routing schemes for different tasks.

In code, it looks like this

class OrchestrationTopology:
    def __init__(self, coordinator, agents, max_handoffs=12):
        self.coordinator = coordinator
        self.agents = agents
        self.max_handoffs = max_handoffs

    def run(self, task):
        queue = [task]
        results = []
        handoffs = 0

        while queue and handoffs < self.max_handoffs:
            current = queue.pop(0)
            plan = self.coordinator.route(current)
            target = plan["agent"]

            if target not in self.agents and target != "finish":
                return self.coordinator.fallback(results, reason="unknown_agent")

            if target == "finish":
                return self.coordinator.merge(results)

            outcome = self.agents[target].execute(plan["payload"])
            results.append(outcome)
            queue.extend(self.coordinator.next_steps(outcome))
            handoffs += 1

        return self.coordinator.fallback(results, reason="handoff_limit_reached")

How it looks at runtime

Request: "Prepare a comparison of 3 CRMs and provide a recommendation for a small business"

Step 1
Coordinator: Decompose -> [data collection, cost evaluation, final recommendation]
Topology: Route -> Agent A (data collection), Agent B (cost evaluation)

Step 2
Agent A: returns a feature comparison
Agent B: returns a comparison of prices and constraints
Coordinator: Merge -> sends to Agent C (final recommendation)

Step 3
Agent C: forms the user response
Topology: Stop -> final_answer + trace

Orchestration Topology does not replace agents; it organizes their collaboration into a predictable process.

When it fits - and when it does not

Orchestration Topologies are needed when tasks are better solved by a team of agents. For simple one-shot tasks, this is usually excessive.

Fits

Does not fit

In such cases, a simple call is often enough:

response = single_agent.run(task)

Typical problems and failures

Most such problems are solved through explicit routing, handoff limits, and transparent trace for each branch.

How it combines with other patterns

Orchestration Topologies describe the architecture of agent interaction, not a replacement for other system layers.

• Agent Runtime - Runtime executes each agent step, while topology defines the route between agents.
• Tool Execution Layer - each agent in the topology calls tools through a controlled execution layer.
• Memory Layer - topology defines which memory is shared and which is local to a specific agent.
• Policy Boundaries - policy rules verify allowed transitions and actions in each branch.

In other words:

• Agent Patterns define how a specific agent thinks
• Orchestration Topologies define how multiple agents execute a task together

How this differs from Orchestrator Agent

Orchestrator Agent is the role of one agent.

Orchestration Topologies are the architectural map of the whole agent system.

In short

FAQ

Q: How do you choose between Pipeline, Parallel, and Hierarchical topology?
A: If steps depend on each other - Pipeline. If subtasks are independent - Parallel. If there are multiple coordination levels - Hierarchical.

Q: Can you start with a simple topology and make it more complex later?
A: Yes. Teams often start with a centralized scheme, then add hierarchy or hybrid branches as task load grows.

Q: Does topology replace policy rules?
A: No. Topology handles routing and coordination, while Policy Boundaries controls which actions and transitions are allowed at all.

Q: What should you choose: Orchestrator Agent or Orchestration Topology?
A: They are not mutually exclusive. Orchestrator Agent can be a node inside the chosen topology.

What Next

Topology explains how a task moves through the system. Next, see how to make that movement controlled and safe:

• Hybrid Workflow Agent - how to combine fixed stages with agent decisions.
• Agent Runtime - how to execute steps with controlled stop reasons.
• Human-in-the-Loop Architecture - where a human should keep final decision authority.
• Production Stack - how to scale topology into production operations.