When should you use a multi-agent system instead of a single agent in 2026?

Use multiple agents only when the next stage can act on a bounded relay without losing the facts needed for its next decision. If it cannot, keep shared context in one agent—or redesign the relay and prove it against a single-agent baseline before adding a handoff.

The direct evidence is early: an arXiv v1 preprint submitted July 17, 2026, not a peer-reviewed production study, reports controlled comparisons of single-agent and multi-agent systems under relay-bandwidth conditions. Its useful lesson is a mechanism, not a deployment promise: reducing redundant context can help, while losing downstream-relevant information can reverse the benefit.

18

controlled comparisons in the preprint

5

benchmarks evaluated

3

model scales evaluated

v1

unreviewed preprint version

Another agent creates an information boundary

A single agent accumulates context in one place. A multi-agent system gives workers isolated local contexts and connects them with relay messages. The preprint argues that an unbounded relay can reproduce the shared-context case; the tradeoff appears when the relay is bounded. Compression may remove distraction, but it can also discard evidence the next worker needs.

In its evaluated settings, near-sufficient relays helped most for weaker models, while relays that lost needed information could shrink or reverse gains, especially for stronger models. Those are benchmark findings, not a promise about your production stack. For Bonfire, this is an AgentOps Hardening question: make the relay an inspectable contract, not a clever summary hidden in a prompt.

Write the relay contract before you add a specialist

A useful relay says more than “research complete” or “send this to the writer.” It gives the downstream stage what it must verify and act on. The following is a Bonfire engineering recommendation informed by the relay-loss mechanism; it is not a universal standard proved by the paper.

A research worker might pass primary-source links, claim IDs, direct excerpts, and unresolved questions to a writer. A fluent summary alone is not enough when the writer must verify a precise claim. The exact fields depend on the next decision—not the names you gave the agents.

Measure the boundary against one agent with shared context

Run representative tasks through a single agent with the shared context it would otherwise retain, and through the proposed multi-agent flow with the intended relay contract. Hold the task, acceptance criterion, and budget policy steady. Then inspect whether the relay changes the downstream decision, accepted outcome, tool arguments, required citations, latency, or cost.

This evaluation approach is an inference from the paper's central mechanism, not a production metric the authors validated. Its capability term is qualitative and its relay-complexity term comes from task- structure analysis, so there is no safe universal relay score, token budget, or team-size threshold to copy into a design review.

Choose orchestration by who owns the next decision

OpenAI's Agents SDK illustrates two common patterns: a manager that calls specialists as tools while retaining final control, and a handoff that transfers control to a selected specialist. Its handoff documentation says full conversation history is passed by default unless an input filter changes it. That is implementation context, not proof that a handoff improves a particular workflow.

Choose a manager when one owner must reconcile evidence and enforce shared controls. Choose a handoff when the specialist should own the next user-facing decision and its narrower instructions are genuinely the right context. In either case, define what crosses the boundary and what does not.

Keep one agent when the relay would hide critical state

Stay with shared context when later work depends on many exact upstream facts, when constraints must remain globally consistent, or when a relay failure cannot be detected before a consequential action. Improve the evidence model, retain immutable references alongside summaries, or keep the workflow under one owner until the handoff can be tested.

Once the boundary is justified, make it observable in an agent workflow graph, treat it with the same care as a production tool contract, and record its decision and outcome in an agent observability dashboard. If the added specialist is a reviewer, evaluate its effect on accepted output rather than commentary alone; reviewer evaluation covers that distinction.