The race to deploy AI is on, but many organizations struggle to balance performance with efficiency. AWS has stepped in with major updates to Amazon Bedrock and Amazon SageMaker AI, offering advanced tools that let any developer build, fine-tune, and deploy AI agents, without the traditional complexity or cost.
The Power of Customization in AI Agents
While businesses often rely on large, generic models, this approach can lead to wasted resources and unnecessary expense. Most AI agent tasks don’t require top-tier intelligence; instead, smaller, specialized models can handle everyday workloads much more efficiently. Until recently, customizing these models with advanced techniques like reinforcement learning was out of reach for most companies.
Amazon Bedrock: Bringing Reinforcement Fine Tuning to Everyone
Amazon Bedrock is changing the game with its Reinforcement Fine Tuning (RFT) feature. This capability empowers organizations to guide AI behavior using feedback-driven training, historically a complex and resource-intensive process. RFT reinforces positive actions and discourages undesired ones, making it perfect for agents tasked with reasoning or handling nuanced workflows.
- Developers can start by choosing a base model, uploading relevant data, and setting a reward function, all without deep machine learning expertise.
- Automated pipelines manage the fine-tuning process, making customization straightforward and scalable.
- Organizations have seen up to 66% accuracy improvements over standard models, with companies like Salesforce reporting even greater results for specialized cases.
Currently, RFT supports the Amazon Nova 2 Lite model, and more options are on the horizon. Early adopters, including Salesforce and Weni by VTEX, are already seeing increased efficiency and accuracy in their AI solutions.
Amazon SageMaker AI: From Months to Days
Amazon SageMaker AI builds on years of innovation by offering new, serverless model customization that dramatically reduces development time. Previously, customizing models meant weeks or months of setup and management. Today, SageMaker AI lets teams focus on results rather than infrastructure.
- Choose between an agentic workflow,where an AI agent helps guide you step-by-step,or a self-directed workflow for more control.
- Access a wide range of advanced tools, including Reinforcement Learning from AI Feedback, Verifiable Rewards, Supervised Fine-Tuning, and Direct Preference Optimization.
- Customize across a variety of models, such as Amazon Nova, Llama, Qwen, DeepSeek, and GPT-OSS.
Organizations like Collinear AI and Robin AI are already reaping the benefits. For example, Collinear AI slashed model experimentation timelines from weeks to days, enabling them to prioritize better data and simulations instead of wrangling infrastructure.
Takeaway: Custom AI for Everyone
Amazon’s enhancements to Bedrock and SageMaker AI mark a turning point for AI agent development. By simplifying the customization process and making powerful tools accessible to all developers, AWS is empowering businesses to create more specialized, responsive, and cost-effective AI agents. This democratization means companies of any size can quickly move from idea to deployment, unlocking AI tailored to their unique needs.
For more details about these capabilities, visit:
- AWS News blog: RFT in Amazon Bedrock
- RFT in Amazon Bedrock product page
- AWS News blog: SageMaker AI serverless model customization
- SageMaker AI model customization product page
How AWS Is Making AI Agent Customization Faster and Smarter