Tag: faang

Synchronizing the Senses: Powering Multimodal Intelligence for Video Search By: Meenakshi Jindal and Munya Marazanye Today’s filmmakers capture more footage than ever to maximize their creative options, often generating hundreds, if not thousands, of hours of raw material per season or franchise. Extracting the vital moments needed to craft compelling storylines from this sheer volume of …

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In today’s agentic AI environments, the network has a new set of responsibilities. In a traditional application stack, the network mainly moves requests between services. But as discussed in a recent white paper, Cloud Infrastructure in the Agent-Native Era, in an agentic system the network sits in the middle of model calls, tool invocations, agent-to-agent …

Read more “Envoy: A future-ready foundation for agentic AI networking”

By Renata Teixeira, Zhi Li, Reenal Mahajan, and Wei Wei On January 26, 2026, we flipped an important switch for Live at Netflix: all Live events are now encoded using VBR (Variable Bitrate) instead of CBR (Constant Bitrate). It sounds like a small configuration change, but it required us to revisit some of the foundational assumptions …

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Evaluating single-turn agent interactions follows a pattern that most teams understand well. You provide an input, collect the output, and judge the result. Frameworks like Strands Evaluation SDK make this process systematic through evaluators that assess helpfulness, faithfulness, and tool usage. In a previous blog post, we covered how to build comprehensive evaluation suites for …

Read more “Simulate realistic users to evaluate multi-turn AI agents in Strands Evals”

Monitoring competitor prices is essential for ecommerce teams to maintain a market edge. However, many teams remain trapped in manual tracking, wasting hours daily checking individual websites. This inefficient approach delays decision-making, raises operational costs, and risks human errors that result in missed revenue and lost opportunities. Amazon Nova Act is an open-source browser automation …

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As AI workloads transition from experimental prototypes to production-grade services, the infrastructure supporting them faces a growing utilization gap. Enterprises today typically face a binary choice: build for high-concurrency, low-latency real-time requests, or optimize for high-throughput, “async” processing. In Kubernetes environments, these requirements are traditionally handled by separate, siloed GPU and TPU accelerator clusters. Real-time …

Read more “Run real-time and async inference on the same infrastructure with GKE Inference Gateway”

Your AI agent worked in the demo, impressed stakeholders, handled test scenarios, and seemed ready for production. Then you deployed it, and the picture changed. Real users experienced wrong tool calls, inconsistent responses, and failure modes nobody anticipated during testing. The result is a gap between expected agent behavior and actual user experience in production. …

Read more “Build reliable AI agents with Amazon Bedrock AgentCore Evaluations”