Organizations building AI agents face a common challenge: Every request, whether a routine status check or complex analysis, typically gets routed to a single large, expensive model. This one-size-fits-all approach can drive up API costs, introduce unpredictable latency, and remove the possibility of offline operation. Even routine queries that could resolve locally end up requiring a full network round-trip to a cloud-hosted large language model (LLM).

The Adaptive Agentic Intelligence Framework (AAIF), built with Strands Agents, takes a different approach. Instead of routing all requests to a single large model, AAIF uses small language models (SLMs) to handle routine tasks locally. More complex requests are routed to cloud-hosted models through Amazon Bedrock. This approach builds on the research position from Belcak et al. at NVIDIA. They argue that small language models are “sufficiently powerful, inherently more suitable, and necessarily more economical for many invocations in agentic systems.” Built on Strands Agents, the framework demonstrates how intelligent model orchestration can help reduce costs while maintaining low latency for local operations.

In this post, we walk through the key components of AAIF using Strands Agents with local SLMs through Ollama and cloud models through Amazon Bedrock. We use an Internet of Things (IoT) monitoring scenario as a reference implementation.

AAIF organizes AI processing into three layers, each optimized for different task types, as Figure 1 shows:

Figure 1: The three AAIF processing layers for smart orchestration, local processing, and cloud processing

Figure 1 shows the complete AAIF system architecture. At the center sits the Strands agent, which connects to an Ollama runtime hosting both the 1B-parameter orchestrator model and the vision model.

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