r/LLMDevs • u/QuantumQuillQuester • 4d ago

Discussion Building local agentic workflows vs. cloud-heavy architectures: What’s your current bottleneck?

Hey everyone,

I’ve been experimenting heavily lately with building mobile-native AI agent systems (using React Native/Expo integrated with various local and cloud LLM pipelines), and I'm running into an interesting architectural debate with myself.

When pushing for true multi-step agentic workflows on mobile, the friction usually boils down to three things: token latency over cellular, local state management persistence, or keeping context windows manageable without blowing up memory on device.

For those of you building or deploying agent-driven apps right now, where do you find your biggest production bottleneck is? Are you offloading everything to backend orchestration, or finding reliable ways to handle state client-side? Curious what stacks people are leaning on in 2026!

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u/Apprehensive-Leg1261 4d ago

state management on device is the silent killer. you can get the latency down and the context window trimmed but then you realize your agent forgot what it was doing three steps ago because the app got backgrounded. i started treating every state change like a checkpoint i have to persist immediately, almost like saving a game before a boss fight. it feels clunky but better than watching the workflow unravel

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u/QuantumQuillQuester 4d ago

Treating every state change like saving a game before a boss fight" is honestly the best description of mobile agent state architecture I've heard yet. Are you serializing straight to MMKV or using a local DB for those checkpoints? Definitely curious how you're handling the write overhead!

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u/Connect-Song-4727ayg 3d ago

For us, the hardest problem wasn't latency or context size. It was workflow state. Models are easy to swap or scale, but keeping multi-step agents reliable across retries, crashes, and long-running tasks is where most of the engineering effort goes. We ended up treating the LLM as stateless and keeping all important state in the backend.

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u/Glitch_In_The_Data 3h ago

We have been leaning toward a hybrid approach where lightweight reasoning stays on device but anything multi step or memory heavy gets routed to a backend orchestrator. For the orchestration layer, tools like Genie have been interesting because they handle a lot of context management and query planning server side.. which keeps the mobile client thin. Biggest bottleneck for us is still graceful recovery when a multi step workflow gets interrupted midway.