Category Archives: AI & Machine Learning
Public code filtering in GitHub Copilot is a governance control for a specific question: what should happen when a Copilot suggestion matches or closely resembles code that is publicly available on GitHub? The answer affects developer workflow, attribution review, and enterprise policy, but it does not make generated code automatically safe or license-cleared. In Microsoft […]
Reusable agent components are valuable when several Copilot Studio agents need the same capability but should not independently reinvent prompts, topics, tools, actions, and integration logic. Microsoft now provides component collections and an Agent Library experience through Copilot Agent Kit, allowing makers to install reusable packages and add them to agents. In Microsoft AI Agents, […]
Safety evaluation is the discipline of testing whether an AI application behaves acceptably before the same failure becomes a production incident. In Microsoft Foundry, this goes beyond checking a chatbot for rude language. The current evaluation stack covers content risks, agent behavior, tool use, groundedness, task completion, and other dimensions that can be run against […]
Semantic ranking in Azure AI Search improves an existing result set by applying a second-stage relevance model to the best candidates returned by keyword, vector, or hybrid retrieval. It is not a replacement for indexing, chunking, vectorization, or first-stage retrieval. In Microsoft AI Agents, this distinction matters because semantic ranking is often one layer in […]
Web search changes a Claude application from a closed-context model call into a system that can acquire current external evidence during the turn. That is powerful for research, news, changing documentation, product availability, and time-sensitive facts, but it also introduces new trust boundaries. In Claude Engineering, the web search tool should be designed as a […]
Amazon OpenSearch Serverless can act as a managed vector retrieval layer for semantic search, recommendation, similarity matching, and retrieval-augmented generation without requiring a team to size and operate conventional OpenSearch clusters. In Generative AI on AWS, the design question is not simply whether a vector store can return nearest neighbors. Teams need to align embedding […]
Human approval is most effective when it controls a specific consequence rather than functioning as a vague “continue?” button. Claude can plan, summarize, and propose actions at machine speed, but some operations still need accountable human judgment: sending external messages, changing production systems, moving money, deleting data, accepting terms, or using sensitive credentials. In Claude […]
A Model Context Protocol server is not simply a collection of functions exposed to Claude. It becomes an integration boundary through which an agent discovers capabilities, reads data, invokes operations, and potentially reaches systems with real side effects. In Claude Engineering, MCP server design should therefore be approached like public API design: define a narrow […]
The quality of an MCP integration often depends less on the transport than on the contract of each tool. Claude sees a name, description, input schema, and eventually a result. From that limited interface it must decide whether the tool is appropriate, construct valid arguments, interpret the outcome, and determine the next step. In Claude […]
A multi-agent Claude system is useful when one conversation is no longer the right unit of work. Research, code analysis, incident investigation, document review, and other broad tasks can often be decomposed into independent workstreams that benefit from separate context and then recombined. In Claude Engineering, the important architectural decision is not how many agents […]
Parallel tool use can remove a surprising amount of latency from a Claude application, but only when the operations are genuinely independent. Claude may return several tool calls in one assistant turn, allowing the application to execute them concurrently and return all results together. In Claude Engineering, parallelism should be treated as a scheduling decision […]
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 […]
Agentic applications are often described as loops—ask a model, call a tool, inspect the result, and continue—but production systems usually need more structure than one in-memory loop can provide. Long-running approvals, retries, timeouts, parallel branches, compensating actions, and external events all become easier to reason about when orchestration state is explicit. In Generative AI on […]
