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Today, we’re announcing a new API with Amazon Bedrock Guardrails. With this API, you can apply individual safeguards, also referred to as safety checks, at any point in your agentic AI applications without creating guardrail resources. The new InvokeGuardrailChecks API gives you the flexibility to invoke supported safeguards at any turn in the agentic loop […]

Today, we’re excited to announce container image caching for Amazon SageMaker AI inference, the next major advancement in our faster scaling optimization journey. This speeds up end-to-end latency by up to 2x for generative AI models during scale-out events. Over the years, Amazon SageMaker AI has continued to reduce latency across these scaling stages: detecting

As large language models (LLMs) grow in size and complexity, maximizing inference throughput while minimizing latency remains a critical challenge for enterprise production deployments. Speculative decoding is one effective strategy to address this, utilizing a lightweight draft model to guess future tokens which are then verified by the target LLM in a single forward pass. While state-of-the-art frameworks like Extrapolation Algorithm for Greater

Today, we are announcing the availability of the Gemma 4 family on Amazon Bedrock. Built by Google DeepMind and released under the Apache 2.0 license, Gemma 4 is a family of open-weight models designed with a focus on intelligence-per-parameter across a broad range of deployment scenarios. The family includes three instruction-tuned variants: Gemma 4 31B,

When your AI agent fails in production, knowing that it failed is only the beginning. The harder question is why it failed and what to fix. Traditional evaluation tells you “this agent scored 60 percent on goal completion,” but leaves you manually reviewing execution traces to understand what went wrong. For teams operating agents at

A common challenge in AI-powered research workflows is depth versus context. If your agent reads ten web pages, its context window (the amount of text a large language model (LLM) can process at once) gets filled with raw content. If it also runs data analysis code, chart-generation logic competes with strategic reasoning for limited space.

Rocket Close is a Detroit-based title agency and appraisal management company within Rocket Companies that provides title insurance, property valuation, and settlement services. As demand for mortgages and loans grew, title operations became a bottleneck in the homebuying process. Time-intensive, state-specific title examinations, combined with manual research and fragmented systems, slowed throughput and made it

Amazon Quick and Cisco Webex MCP servers can turn meeting prep and follow-up into a single conversational workflow. Instead of switching between Webex meetings, Vidcast videos, transcripts, recordings, and message spaces, users ask one assistant to gather the context they need. This post shows how to build a custom meeting prep and follow-up assistant using

Organizations process millions of documents daily, from insurance claims and invoices to legal contracts and medical records. While traditional optical character recognition (OCR) solutions extract text, they can’t understand context, relationships, or meaning embedded within complex documents. This limitation creates bottlenecks that require manual intervention, increasing processing time and costs while introducing potential errors. Amazon