Beyond LLMs: How Small Decision Models Make AI Agents Smarter and Faster
Show notes
In this episode of The New Stack podcast, recorded at AGNTCon + MCPCon in San Jose, Nick Lucchesi speaks with Mike Chambers, Senior AI Developer Experience Engineer at AWS, about how small decision models could make AI agents faster, more efficient, and cost-effective.
Unlike large language models (LLMs) that generate responses through multiple processing steps, decider models quickly evaluate information and return probability-based decisions. Chambers explains how these models can improve agent performance by routing requests to appropriate LLMs, enforcing safety guardrails, and determining when agents should retrieve information from memory.
He also shares experiments using AWS Strands Agents and Strands Decider to reduce unnecessary memory lookups and keep agent context relevant. While these models offer significant efficiency advantages, Chambers emphasizes the importance of evaluation, monitoring, and human oversight, particularly for high-risk decisions involving financial transactions.
Looking ahead, Chambers envisions a future where developers combine specialized AI models rather than relying exclusively on LLMs, paving the way for more capable, adaptable agents and applications.
Learn more from The New Stack around the latest developments around small decision models and its impact on AI:
SLMs vs. LLMs: Why Smaller AI Models Win in Business
AWS launches a local answer to TypeSafe’s Jev decision model
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