Strands Agents demo for routing work between local and cloud models
- AAIF uses a local llama3.2:1b model as a request classifier in a Strands Agents setup, directing work to local tools or Amazon Bedrock instead of sending every request to one cloud model.
- In the IoT example, Ollama models handle sensor-telemetry retrieval and camera-feed analysis on local hardware, while a separate Bedrock tool serves requests that need broader context and multi-step reasoning.
- The design is presented for edge hardware, including a Raspberry Pi with 8 GB of memory, as well as cloud-only and hybrid deployments, with the routing and processing components configurable independently.
- For an air-gapped installation, the post proposes replacing the cloud tool with a larger local model; it says a 3B model can cover moderately complex work but may reason less deeply on the hardest queries.
Our take
Compared with sending every request to Amazon Bedrock, hybrid routing moves routine inference onto hardware the team operates. The post estimates that roughly 80% of traffic could avoid a paid API call, but provides no measured cost or latency results. This approach also requires operating Ollama and local models; an air-gapped setup trades cloud access for weaker reasoning on the hardest requests.
Sources
- Adaptive agentic intelligence framework using Strands Agents · AWS Machine Learning
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