Most AI agent frameworks today assume environments with:
- dynamic runtimes
- long-lived processes
- large dependency trees
- forgiving memory behavior
That works fine in the cloud, but breaks quickly when you push into embedded,
edge, or latency-sensitive systems.
When memory budgets drop into the single-digit MB range and startup time
matters more than throughput, very different problems dominate:
- cold start time can exceed useful execution
- memory fragmentation becomes a hard failure mode
- dependency resolution costs more than the work itself
- predictable restarts matter more than flexibility
Exploring this constraint space forced several uncomfortable tradeoffs:
- static linking over dynamic composition
- fewer abstractions, more explicit control
- deterministic memory usage over convenience
- language choice becoming architectural rather than ergonomic
I’m curious how others here think about agent or planner-style systems under
these kinds of constraints. If you’ve tried pushing higher-level logic into
embedded or edge environments:
- what broke first?
- which assumptions didn’t survive contact with hardware?
- what design choices actually held up?
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