Rovo Chat lacks consistent guardrails — falls back to general LLM knowledge instead of staying scope

[Suganya Krishnasamy]

I'm currently exploring and learning agentic AI frameworks, and while testing Rovo Chat's guardrail behavior as part of that learning, I noticed some inconsistencies in how it enforces domain/scope boundaries.

Rovo Chat currently answers out-of-domain questions by falling back to the underlying LLM's parametric (pre-trained) knowledge rather than staying grounded to indexed organizational context (Teamwork Graph / connected knowledge sources). This fallback behavior seems inconsistent depending on the topic category, suggesting category-specific filters rather than a genuine domain-relevance check.

Examples (generalized):

Asked about a public figure's role at an unrelated company - answered directly from parametric knowledge, ungrounded in any indexed Atlassian data.
Asked to generate a full script for an unrelated coding project - complied, functioning as a general-purpose code assistant rather than staying scoped to work-context tasks.
Asked about a different type of public-figure/role query, in a more politically sensitive category - declined and redirected to an external source. This is inconsistent with example 1, since both are equally out-of-domain, ungrounded queries.

Could Atlassian add a genuine domain/scope-relevance guardrail to default Rovo Chat, one that evaluates whether a query is grounded in Atlassian/org knowledge sources before falling back to ungrounded LLM generation, rather than applying category-specific content filters?

Domain-scoped AI assistants typically implement a hard guardrail, such as a scope-relevance classifier or system-level instruction, that rejects out-of-domain queries outright regardless of topic sensitivity, rather than relying on grounding/RAG only when convenient and falling back to raw LLM output otherwise. This reduces hallucination risk, keeps outputs auditable and traceable to a knowledge source, and avoids the assistant being used as an unscoped, ungrounded general-purpose tool.

Has anyone else noticed this inconsistency while building or testing Rovo agents? Would appreciate any suggestions, workarounds, or views on how you're handling scope/guardrail enforcement in your own agentic setups.

[Ajay _view26_]

Hi @Suganya Krishnasamy 

Rovo Chat is built on top of a general-purpose LLM, and the way it grounds responses to your org's Teamwork Graph is through a retrieval-augmented generation (RAG) layer.

When a query doesn't match anything in the indexed organizational data, the model does fall back to its pre-trained knowledge rather than just saying "I don't know."

That's a design choice Atlassian made — it keeps Rovo useful as a general assistant within the workspace, but you're right that it blurs the line between "grounded answer" and "general LLM answer.

For grounded answers you can create a Rovo agent using Rovo Studio or Forge to add a genuine domain/scope-relevance guardrail to the Model calls.