Category Archives: AI & Machine Learning

Claude vision is most useful when an image is part of a larger workflow rather than a one-off screenshot question. Production systems need to decide how images enter the request, how resolution affects cost, what metadata is retained outside the model, how results are verified, and when a human should review the interpretation. In Claude […]

Amazon SageMaker AI endpoint autoscaling turns model serving capacity into a feedback system. A production variant can add or remove instances as demand changes instead of forcing operators to choose one fixed instance count for every hour of the day. In Generative AI on AWS, that is useful for custom models, embedding services, classifiers, rerankers, […]

Amazon SageMaker AI Model Registry is a control point for deciding which trained model artifact is allowed to move toward production. Training systems can generate many candidate models, but production needs a smaller set of versioned, reviewed artifacts with enough metadata to explain what changed and why one version was approved. In Generative AI on […]

AI applications collect credentials at nearly every boundary: model providers, vector stores, relational databases, observability platforms, SaaS APIs, webhook destinations, and custom tools. On AWS, those credentials should not be treated as ordinary configuration. In Generative AI on AWS, the durable pattern is to keep sensitive material outside prompts and source code, give workloads a […]

GitHub Copilot CLI brings agentic assistance into the terminal, where developers already build, test, inspect repositories, and automate repetitive work. Current GitHub documentation supports both an interactive `copilot` experience and non-interactive execution with a prompt, plus dedicated workflow execution for dynamic workflows. In Microsoft AI Agents, the important design question is not whether the CLI […]

GitHub Copilot content exclusion lets organizations tell supported Copilot surfaces to ignore specified files or paths. It is a governance control for reducing the chance that sensitive or out-of-scope repository content becomes model context. In Microsoft AI Agents, the most important point is that exclusion is not universal: support varies by client and mode, and […]

Model Context Protocol gives GitHub Copilot a standardized way to connect to external tools and data sources. GitHub supports MCP across major Copilot surfaces, including IDEs, Copilot CLI, the Copilot app, and cloud-agent experiences, with local and remote server options depending on the client. In Microsoft AI Agents, MCP should be treated as an integration […]

Groundedness detection asks a precise question: does the generated answer stay supported by the context that was supplied to the model? In retrieval-augmented generation, that is different from asking whether the answer is fluent, relevant, or complete. Microsoft Foundry and Azure AI Content Safety provide several groundedness-oriented evaluation paths, making this a practical quality control […]

Hallucination testing is more useful when it is built around concrete failure cases than when it relies on a vague instruction to “check factuality.” Microsoft Foundry supports reusable evaluation datasets, model and agent evaluators, synthetic test generation, trace-derived data, and CI/CD-oriented reruns. For Microsoft AI Agents, that makes a hallucination test set an engineering asset […]

AI applications accumulate secrets faster than conventional web services because the model is only one dependency in a larger execution path. A production workload can call model endpoints, search services, databases, queues, observability systems, SaaS tools, and custom APIs during a single user interaction. In Microsoft AI Agents, the security problem is therefore not simply […]

Managed identity changes the way an AI application proves who it is. Instead of shipping a client secret with the workload, an Azure-hosted service can ask Microsoft Entra ID for a token that represents a platform-managed identity. In Microsoft AI Agents, that pattern is especially valuable because an agent rarely talks to only one model […]

Model Context Protocol servers give Microsoft agents a standardized way to discover and invoke external tools, but the protocol does not remove the need for ordinary application security. An MCP server can expose read operations, write operations, administrative actions, and business workflows through one tool surface. In Microsoft AI Agents, the practical question is therefore […]

Model deployment quota in Microsoft Foundry is capacity planning expressed as an application constraint. A model may be available in the catalog and a deployment may be technically valid, yet the application can still fail under load because its assigned tokens-per-minute or request rate is too low for real traffic. In Microsoft AI Agents, quota […]

OpenSearch neural search changes the retrieval problem from “which documents contain these words?” to “which documents are semantically closest to what the user meant?” That distinction is important for generative AI because a RAG system can produce fluent output from poor evidence. In Generative AI on AWS, Amazon OpenSearch Service can provide semantic and hybrid […]

Private networking for generative AI on AWS is less about hiding one endpoint and more about controlling the complete data path. A production request may begin in an application subnet, call Amazon Bedrock, retrieve documents from Amazon OpenSearch Service or S3, read credentials from Secrets Manager, use KMS-protected data, and emit logs or traces before […]