Best Practices for Designing Multi-Agent System Topologies

A well-designed multi-agent topology reads like clean code: anyone on the team can glance at the graph, understand the data flow, and predict how the system behaves under stress. The habits below cost almost nothing to adopt and repay the effort many times over as graphs grow from two agents to twenty.

Adopt a naming convention before you draw your first node. Use short, lowercase, underscore-delimited identifiers that describe the job, not the model — researcher, draft_writer, qa_reviewer. Long names clutter the canvas and the exported Python. Never rename a node casually: every edge references its name, so a rename is a refactor that must update every connected edge in the same commit.

Set the temperature once per node and document why. Extraction, routing, and classification agents should run cold, between 0.0 and 0.3, so the same input reliably produces the same branch. Drafting agents can run warmer, but a writer above 0.8 will hallucinate structure. Record the intended temperature in the role description so a later engineer knows whether a value is deliberate or a leftover default.

Keep the graph acyclic by discipline. A self-referencing edge is an infinite loop; the harness rejects source-equals-target edges outright. Longer cycles are harder to spot, so establish a review habit: before wiring any edge, ask whether a downstream agent could ever feed its own upstream. If a feedback loop is genuinely required, model it as a bounded retry counter on a conditional edge instead of a raw cycle.

Define the context contract at every hand-off. Sequential edges pass the upstream context forward, so decide whether the downstream agent sees the full history, a distilled summary, or only a single field. Consistent context shaping prevents two common diseases: context bloat, where every agent re-reads megabytes of history, and context starvation, where a required field silently disappears between hops.

Use parallel edges only where results join cleanly. Parallel fan-out saves latency, but it requires a merge step that combines multiple outputs without contradiction. If two parallel agents may return conflicting facts, add a reconcile stage or accept that the last-writer-wins behavior will occasionally surprise you. When in doubt, keep the branch serial.

Prefer conditional edges over bespoke plumbing. The moment a pipeline needs if/then behavior, route it through a conditional edge rather than hard-coding the branch in a step's prompt. Conditional edges keep the graph legible, make the routing criteria explicit, and produce cleaner exported skeletons with a run_conditional helper ready to use.

Prune orphans and sinks intentionally. An orphan agent has no incoming edge and never receives context; it is either a mistake or a manually-invoked entry point. A sink with no outgoing edge is a terminal — fine if it emits the final answer, but a bug if it silently swallows results that should continue downstream. Review the node and edge counts after every edit to confirm nothing was left disconnected.

Version your topology with the text export. The formatted topology text is diff-friendly, which means you can put it in version control, review changes in pull requests, and roll back a regression by reverting a one-line edge change. Commit the Python skeleton alongside it so the scaffold and the graph never drift apart.

Finally, measure before you expand. Log the latency, token spend, and failure rate of each edge in production, then grow the graph only where the numbers justify it. The best topologies are minimal topologies: each node earns its place by demonstrably improving output, not by being a showcase for the framework.