From the source
- The framework answers a cost problem: sending every request, even a routine status check, to one large cloud model drives up API costs, adds unpredictable latency and rules out offline operation.
- AAIF splits processing into three layers, with a 1B-parameter llama3.2:1b orchestrator classifying each request and dispatching it without hardcoded rules.
- Local tools handle device telemetry and camera-feed vision through Ollama, while a get_expert_insights tool calls Amazon Bedrock through boto3 for multi-step analysis.
- The setup runs on a Raspberry Pi with 8 GB of memory, entirely in the cloud, or in a hybrid mix, because routing logic is separated from the tools.
- The post cites Belcak et al. at
NVIDIA, who argue small language models are sufficiently powerful and more economical for many agentic invocations.
