Custom Rovo Agent — LLM output truncation causes corrupted JSON in action inputs for large content

Question

When a Forge custom Rovo Agent is asked to generate a large volume of structured content (e.g. a full course with multiple sections and components) in a single conversation turn, the LLM that powers the Rovo Agent silently truncates its output mid-generation. Because action inputs are constructed from the LLM's raw output, the truncated content results in malformed (non-parseable) JSON being passed to the action handler ,causing the action to fail or receive incomplete data. So i need to know if :

* Is there an official, documented token/character limit on the LLM output that populates action inputs for Forge Rovo Agents?
* Is there a plan to expose a structured truncation signal to action handlers?
* Is there an alternative solution to maintain the generated large content with the same number of components?

Dr Valeri Colon (Connect Centric) · Answer

@Habib ZOUARI appreciate you sharing that—what you’re seeing is pretty consistent with how others are hitting the edges right now. You’re right, chunking solves the truncation problem, but shifts complexity to orchestration and consistency. Today, the most reliable pattern I’ve seen is combining:

bounded generation per step (strict size + schema)

server-side validation/retry

and stateful assembly outside the LLM

It’s not elegant, but it’s stable.

Agree with you on the need for structured truncation signals or streaming-safe outputs—that would remove a lot of this overhead. Definitely worth sharing your feedback with Atlassian, as this is a real builder pain point.

Habib ZOUARI · Answer

@Dr Valeri Colon _Connect Centric_

Thank you for the detailed response — really appreciate you taking the time to clarify this.

We're already working with a chunked approach, but it still comes with its own set of limitations and edge cases that are tricky to handle reliably on the client side. Hopefully the platform evolves to offer better guarantees around output integrity and structured truncation signals in the future.

Thanks again!

Dr Valeri Colon (Connect Centric) · Answer

@Habib ZOUARI this isn’t just a “token limit” issue—what you’re hitting is a mix of LLM output limits + Rovo action payload constraints, and there’s no documented hard limit or truncation signal today. Large single-turn JSON outputs aren’t reliable. The current pattern is to generate in chunks or stages, then pass smaller, validated payloads to actions.

Unfortunately, there isn’t a way yet to preserve full volume + quality in one pass without tradeoffs.