Aws introduces Adaptive agentic intelligence framework with Strands Agents
The AWS Machine Learning Blog presents the Adaptive Agentic Intelligence Framework (AAIF), built with Strands Agents, to orchestrate AI requests between small local models and cloud‑hosted foundation models. The framework uses a 1‑billion‑parameter llama3.2 model running via Ollama as a lightweight orchestrator that classifies each incoming request. Routine tasks such as device telemetry…
Key points
- AAIF routes routine requests to a local llama3.2:1b model via Ollama, reducing cloud API calls.
- Complex queries are sent to Amazon Bedrock frontier models through a Bedrock tool.
- Framework can save costs on roughly 80 % of traffic and runs on edge devices like a Raspberry Pi.
AAIF follows research by Belcak et al. at NVIDIA, which argues that small language models are sufficient and more economical for many agentic invocations. The blog provides a step‑by‑step walkthrough, including prerequisites (AWS account, Ollama, Python 3.10+, Strands Agents library) and sample code that defines three tools: telemetry, vision, and expert insights. By routing roughly 80 % of traffic locally, the pattern can cut paid API usage while still offering cloud‑scale reasoning when needed. The post also outlines how to expose tools via Model Context Protocol (MCP) servers for larger deployments and how to apply Bedrock Guardrails for consistent safety across both tiers.
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Adaptive agentic intelligence framework using Strands Agents
AWS Machine Learning Blog · 21 October 2026
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