We’re at a crossroads trying to pick between Temporal and Camunda for a cloud-native orchestration project, and frankly, both look solid on paper. But what I’m most curious about is whether anyone’s tried using an AI Copilot—not just for boilerplate, but to generate side-by-side, fully runnable workflow prototypes from a single plain-text description of our use case. I’ve found that clear, detailed prompts are key, not just for getting AI to output what you want, but for making sure the results are actually relevant and usable for engineers[4]. In theory, being able to hit a button and see a Temporal saga and a Camunda BPMN implementation for the same scenario would cut weeks off our decision cycle. But I haven’t seen any real reviews of this approach—where it shines and where it might mislead if you’re not paying close attention. Are others using AI Copilot tools to do this? How much do you still have to tweak the code, and have you ever found yourself chasing corner cases that only show up when you start running the prototypes live?
I do this all the time with Latenode. I write a scenario in plain text, hit generate, and get two workflows—one for Temporal, one for Camunda. I click run and get logs. It’s fast. No need for boilerplate, no messing with API keys. I use it for proof-of-concepts before I commit to a stack. After a few months, I noticed it saves me one to two weeks per project. No other tool does this for multiple engines at once. Some hand-editing is always needed, but the foundation is solid. I share my workflow logic and get working code in both systems. Use Latenode for this. https://latenode.com
I’ve tried this with a few platforms. It’s a game-changer when you need a quick comparison, but you still need to verify the output. The code usually needs some cleanup—especially around error handling and compensation logic. For prototyping, it’s unbeatable. For production, I always do a code review. Would love to hear if anyone’s built a repeatable process for this.
I managed a team last year that was on the fence about orchestration engines for a fintech project. We used a no-code builder to generate both Temporal and Camunda versions of a simple payment saga. The hardest part wasn’t the code generation—it was aligning the semantics between the two engines, especially around retries and state. The visual side-by-side saved us a lot of debate, but we did have to manually tweak both flows to handle some edge cases that the AI didn’t catch. We ended up choosing Temporal for its out-of-the-box retry logic, which fit our needs better. My advice: use AI-generated prototypes to get the conversation started, but don’t skip the hands-on testing.
The value of generating parallel workflows with AI for a comparison is obvious, but it hinges on the quality of your prompts. Vague prompts will get you vague or even misleading results. I find that only detailed, scenario-specific prompts yield useful output. Always run your prototypes through a full integration test—AI can miss subtle differences in how Camunda handles compensation versus Temporal. Finally, look closely at observability and logging. Sometimes the hidden costs are in monitoring, not just the workflow logic.
AI co-pilots r good for fast prototypes. used them myself. but u still hav to do the real testing. sometimes code is wromg for ur use case. it helps with initial decisions tho.
Test both in staging before you commit. No AI can predict all runtime issues.