Category Archives: AI & Machine Learning
Amazon Aurora PostgreSQL with pgvector gives teams a vector-search path inside a relational database they may already operate. In 2026, AWS guidance for production pgvector workloads emphasizes HNSW for most online retrieval, IVFFlat for selected memory-sensitive or build-cost scenarios, and no approximate index at all for small datasets or cases where exact recall is more […]
Amazon Bedrock Agents turn model reasoning into action through action groups. An action group describes functions or an OpenAPI-defined API surface that the agent may choose during orchestration. Bedrock can pass the requested action to a Lambda function for fulfillment, or it can return control to the application so the application decides how to execute […]
The Amazon Bedrock Converse API provides a common conversational interface across supported foundation models. Instead of building a separate request shape for every provider, an application can send messages, system prompts, shared inference parameters, tool configuration, guardrail configuration, and request metadata through Converse or ConverseStream. Model-specific parameters are still available when needed, but the main […]
Amazon Bedrock Flows provides a visual and API-defined way to build end-to-end generative AI workflows from connected nodes. A flow can combine prompts, Bedrock Agents, knowledge-base retrieval, Lambda functions, S3 retrieval, inline code, conditions, iterators, collectors, and other supported nodes. The result is an explicit execution graph that can be versioned, aliased, and invoked from […]
Automated Reasoning checks in Amazon Bedrock Guardrails validate generated statements against formal logic derived from a policy document. Instead of asking another model whether an answer “looks correct,” the service translates natural-language content into variables and logical relationships, then evaluates those relationships against explicit rules. It is designed for domains where correctness can be expressed […]
AlloyDB AI adds vector search directly to a PostgreSQL-compatible relational database. It supports the pgvector programming model, standard HNSW indexing, and a Google-developed ScaNN index through the alloydb_scann extension. The attraction is straightforward: applications can keep relational attributes and embeddings together instead of moving operational data into a separate vector database solely for semantic retrieval. […]
Large language model capacity is usually measured in tokens, not only requests. Two API calls can consume radically different amounts of model capacity because one contains a short question and the other includes a long document, several tool definitions, and a large response. Azure API Management addresses this mismatch with an LLM token-limit policy that […]
AI transformation becomes difficult to defend when the organization can describe what it deployed but cannot explain what changed. Agent counts, prompt volume, token spend, and Copilot licenses are activity metrics. They do not by themselves prove business value. Microsoft’s current guidance on agent ROI starts from a more disciplined premise: define value before building, […]
When a company has one AI application, it is easy to connect that application directly to a model endpoint. The design changes when dozens of applications, agents, teams, and tenants share model deployments. They need a common place to authenticate callers, allocate capacity, observe consumption, enforce policy, route traffic, and protect backends from bursts. Azure […]
Microsoft’s agent platform is no longer one product with one runtime. It is a set of engineering surfaces that let teams choose how much orchestration they want Microsoft to manage and how much they want to own. Microsoft Foundry Agent Service can run prompt agents, voice-based prompt agents, and hosted agents, while the Responses API […]
Agent-to-agent communication becomes difficult when every team invents its own invocation contract. One agent exposes a REST endpoint, another expects a function call, a third returns a proprietary task object, and none of them describe capabilities in the same way. The Agent2Agent protocol is meant to reduce that friction by giving agents a standard way […]
Conversation state is what lets an agent behave as though the second turn belongs to the first. It sounds simple until the application has to decide which messages belong together, how long they should persist, what happens when two requests arrive at once, and whether the conversation is allowed to contain sensitive data. In Microsoft […]
Retries are one of the easiest ways to make an agent system look more reliable while quietly making it less safe. A transient 429 from a model deployment may deserve another attempt. A malformed tool request does not. A timeout after a payment API call is especially dangerous because the call may have completed even […]
A shared agent endpoint does not imply shared user state. In a production Microsoft Foundry deployment, session isolation is the boundary that keeps one caller’s conversations, files, runtime state, and stored data from appearing in another caller’s workspace. This matters most in enterprise and multi-tenant systems, where the agent may look like one service from […]
Semantic caching changes the economics of an AI gateway by allowing it to reuse an earlier model response for a new prompt that is similar in meaning, not merely identical in text. Azure API Management supports this pattern with LLM semantic cache lookup and store policies backed by an external vector-capable cache. The appeal is […]
