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This post shows how to deploy a multimodal WhatsApp ordering assistant built with Amazon Bedrock AgentCore and Amazon Nova 2. Many quick-service restaurants spread ordering across an app, a website, a phone line, and the counter. Each of those is a separate system to build and run. Each one also fragments the customer’s history, making […]

Memory lifecycle policies help long-running agents on Amazon Bedrock AgentCore stay effective by systematically managing what they remember and forget. Your agent generates memories from every conversation it conducts. If you don’t actively manage these memories, your agents will accumulate outdated context, which can degrade response quality and create compliance risks for your deployment. After

A Physical AI system, such as a robot or autonomous vehicle (AV) that translates real-world data into physical actions, can’t be built in a single training job. Instead, it takes a continuous pipeline: a loop of generating synthetic data, post-training perception and policy models, so the system understands its surroundings and can act, and evaluating

If you run foundation model (FM) workloads on Amazon SageMaker HyperPod, you know the work is rarely a single task. It is a chain of dependent ones. An infrastructure team creates the network and control plane, attaches accelerator capacity, and installs cluster dependencies in the right order. It also prepares storage and identity, keeps distributed

For customer service teams handling thousands of utility bills each month, accurately parsing and analyzing complex, multi-page documents is a persistent challenge. Inconsistent formats, dense tables, and varied layouts make it difficult to extract the right information quickly. This leads to delayed responses, billing errors, and frustrated customers. As document volumes grow, these inefficiencies compound,

Disaster recovery (DR) at scale is hard. When thousands of microservices span multiple AWS Regions, coordinating a reliable failover becomes a major operational challenge. At Intuit, we operate at this scale. We support products that millions of people rely on to run their businesses and manage their finances. These include TurboTax, QuickBooks, Mailchimp, and Credit

Engineering teams adopting the AI-Driven Development Lifecycle (AI-DLC) with Amazon Bedrock AgentCore and coding agents like Kiro often struggle with the gap between conceptual frameworks and working code. Amazon Bedrock AgentCore is a service for building, connecting, and optimizing agents at scale with any framework or model. AI-DLC positions AI as a central collaborator across

An agent that works in a notebook isn’t an agent in production. After real users arrive, you own work that has nothing to do with your agent’s reasoning. Keep one user’s session out of another’s, and hold state across turns and days. Auth for every tool the agent calls sits in your code, and the

AI is changing the shape of the workday, and effective implementations quietly remove work, which can lead to meaningful time savings. According to a recent Gartner study, AI saves sellers nearly five hours a week. Email is often where that shift shows up first. As a company grows across teams and time zones, email grows

OpenAI ChatGPT Codex with LiteLLM can provide centralized enterprise controls for generative AI coding agents. These agents help developers understand repositories, write code, run tests, and complete multi-step engineering tasks. As organizations move from individual experimentation to managed adoption, teams need a consistent way to control model access and attribute consumption. They must also apply