Principle:Microsoft Autogen Graph Topology Design
| Knowledge Sources | |
|---|---|
| Domains | Multi-Agent Systems, Graph Theory, Workflow Orchestration, Directed Graphs |
| Last Updated | 2026-02-11 00:00 GMT |
Overview
Graph topology design is the practice of defining the structural layout of nodes and directed edges that govern how agents communicate and execute within an orchestrated workflow.
Description
In multi-agent systems, the order and conditions under which agents execute can be modeled as a directed graph (digraph). Each node in the graph represents an agent, and each directed edge represents a permitted transition from one agent to another. This topological structure determines:
- Execution order: Which agent runs first, second, and so on.
- Parallelism: Which agents can execute concurrently (fan-out patterns).
- Convergence: Where multiple execution paths must synchronize before proceeding (fan-in / join patterns).
- Conditional branching: Which edges are traversed based on runtime conditions evaluated against agent output.
- Cycles: Iterative loops where agents revisit earlier stages, with mandatory exit conditions to prevent infinite execution.
A well-designed graph topology ensures that every agent is reachable from at least one entry point, that the graph has at least one terminal (leaf) node to guarantee eventual completion, and that any cyclic path contains at least one conditional edge providing an exit.
The key structural properties that must be validated include:
- Start nodes: Nodes with no incoming edges serve as entry points. Alternatively, a single default start node can be designated explicitly.
- Leaf nodes: Nodes with no outgoing edges serve as natural termination points.
- Edge consistency: A node must have either all conditional or all unconditional outgoing edges; mixing is not allowed.
- Cycle safety: Every cycle in the graph must contain at least one conditional edge to ensure an exit path exists.
- Activation semantics: When multiple edges point to the same target node, the activation mode ("all" or "any") determines whether the target waits for all predecessors or fires on the first available.
Usage
Use graph topology design when:
- You need to orchestrate multiple agents with complex interdependencies rather than simple sequential or round-robin execution.
- Your workflow requires conditional branching where different agents handle different cases based on prior agent output.
- You need fan-out parallelism where one agent's output is simultaneously processed by multiple downstream agents.
- You need fan-in synchronization where an agent must wait for all upstream agents to complete before proceeding.
- You have iterative review loops where an agent's output is evaluated and potentially sent back for revision.
- You want a declarative, inspectable representation of your workflow that can be validated before execution.
Theoretical Basis
Graph topology design for agent orchestration draws from graph theory and workflow modeling:
Directed Acyclic Graphs (DAGs)
The simplest case is a DAG, where execution follows a topological ordering. Each node executes only after all its predecessors have completed. This guarantees termination and deterministic ordering.
Cyclic Directed Graphs with Exit Conditions
When cycles are introduced (e.g., review loops), the graph is no longer acyclic. To ensure termination, every cycle must contain at least one conditional edge. The condition evaluates agent output at runtime and determines whether to continue the loop or exit to a different branch.
Activation Semantics
When a node has multiple incoming edges, two activation modes apply:
- "all" (AND-join): The node waits until all incoming edges in its activation group have been satisfied. This implements a synchronization barrier.
- "any" (OR-join): The node fires as soon as any single incoming edge in its activation group is satisfied. This implements a race pattern.
Activation groups allow fine-grained control when the same target node is reachable via multiple independent paths (e.g., one from an initial entry and another from a loop-back edge).
Pseudocode: Graph Validation
function validate_graph(graph):
assert graph.nodes is not empty
assert graph has at least one start node (no incoming edges)
assert graph has at least one leaf node (no outgoing edges)
for each node in graph:
edges = node.outgoing_edges
has_conditional = any(edge.condition != None for edge in edges)
has_unconditional = any(edge.condition == None for edge in edges)
assert not (has_conditional and has_unconditional) # no mixing
for each cycle in graph:
assert at least one edge in cycle has a condition # exit path required
Pseudocode: Topological Execution
function execute_graph(graph, task):
ready_queue = graph.start_nodes()
remaining = compute_incoming_edge_counts(graph)
while ready_queue is not empty:
current_nodes = drain(ready_queue)
execute in parallel: [run(node, task) for node in current_nodes]
for each completed node:
for each outgoing edge from node:
if edge.condition is satisfied by node output:
decrement remaining[edge.target]
if remaining[edge.target] == 0:
ready_queue.add(edge.target)
return collected results