How Atlassian's Rovo Chat evolved into a hierarchical multi-agent orchestration framework
Rovo Chat's original single-agent framework was static and failed to generalize as queries became more complex, with a single orchestrator easily getting confused when managing tools across many domains.
A multi-agent DAG orchestration approach proved brittle when subagents failed or did not supply the information needed by downstream tasks, because a complete orchestration plan could not be reliably generated from the user query alone in a single shot.
The hybrid multi-agent orchestrator achieved a +3.49% quality improvement and reduced P50 first-token latency by 29.5% and P90 latency by 19.97% compared to the single-agent RAG baseline.
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Frequently asked questions
What did this team achieve with this AI workflow?
The hybrid multi-agent orchestrator achieved a +3.49% quality improvement and reduced P50 first-token latency by 29.5% and P90 latency by 19.97% compared to the single-agent RAG baseline.
What tools did this team use?
Rovo Chat, LLMs, RAG, Jira, JQL.
What results were reported?
response correctness improvement — Hybrid Orchestrator vs single-agent baseline: +3.49%; response correctness improvement — DAG Orchestrator vs single-agent baseline: +2.52%; P10 first-token latency change — Hybrid Orchestrator vs baseline: -75.96%; P10 first-token latency change — DAG Orchestrator vs baseline: -71.7% (source-reported, not independently verified).
What failed first in this deployment?
A multi-agent DAG orchestration approach proved brittle when subagents failed or did not supply the information needed by downstream tasks, because a complete orchestration plan could not be reliably generated from th…
How is this back office ops AI workflow structured?
User submits query to Rovo Chat → LLM selects reasoning mode → Orchestrator decomposes complex queries → Route subtask to domain subagent → Domain subagent executes specialized task → Response delivered to user.