Author Archives: Allen Rodriguez

Agentic AI engineering begins where ordinary prompt-response integration stops. An agent does not merely generate text; it can select tools, retrieve external context, maintain working state, request approval, retry a failed step, and continue until an application-defined objective is met. That additional autonomy changes the engineering problem. The model becomes one decision-making component inside a […]

Amazon AWS AIP-C01: Vector Search with OpenSearch Serverless For AWS AIP-C01 retrieval design, Amazon OpenSearch Serverless can act as a managed vector layer for semantic search, similarity matching, and retrieval-augmented generation without requiring a team to operate conventional OpenSearch clusters; index quality, filters, and permissions still require explicit design. In Generative AI on AWS, the […]

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. Parallel tool use in Claude Engineering is therefore a correctness-constrained scheduling […]

Microsoft AI-103: Safety Evaluation in Microsoft Foundry 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 […]

Microsoft AI-103: Multi-Agent Orchestration in Foundry Multi-agent orchestration in Microsoft Foundry is becoming less about drawing a visually impressive workflow and more about choosing an execution model that can survive production constraints. Microsoft Foundry Agent Service can host and scale agents, while Microsoft Agent Framework provides orchestration patterns such as sequential, concurrent, handoff, group chat, […]

Power Platform Data Loss Prevention for Agents Power Platform data policies become more consequential when Copilot Studio agents can call connectors, access knowledge, publish to channels, react to events, and invoke automation without a person choosing every step. A policy that once governed an app maker’s connector selection can now affect which operations an autonomous […]

Managing Copilot Studio Agents Across Power Platform Environments Managed Environments can turn a collection of Copilot Studio projects into an administrable platform boundary with consistent security, policy, and lifecycle controls. Agents reside in Power Platform environments that define data policies, roles, connection policies, and lifecycle separation. Managed Environments extend these governance capabilities when agents move […]

Microsoft AI-103: Multimodal Document Extraction Multimodal document extraction is not simply OCR with a larger model. Business documents combine text, page layout, tables, selection marks, figures, charts, handwriting, headers, and spatial relationships that change meaning when flattened into a plain string. For Microsoft AI agents, an extraction pipeline should preserve document structure before adding generative […]

A Model Context Protocol server is not simply a collection of functions exposed to Claude; it defines a capability and authorization boundary that the agent may invoke. It becomes an integration boundary through which an agent discovers capabilities, reads data, invokes operations, and potentially reaches systems with real side effects. A production Claude Engineering MCP […]

Microsoft AI-103: Azure AI Search Semantic Ranking Semantic ranking in Azure AI Search applies a second-stage relevance model to the candidate results returned by keyword, vector, or hybrid retrieval. It cannot recover relevant content that the first retrieval stage excluded, and it does not replace indexing, chunking, or vectorization. In Microsoft AI Agents, semantic ranking […]

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. Web search in Claude Engineering should be governed as external retrieval, with […]

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. The central Claude Engineering decision is not how many agents to create. […]

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. For Claude […]

An MCP integration depends less on transport than on the precision, validation, and authorization boundary of each tool contract. 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. A Claude […]

GitHub Copilot Public Code Filtering: Risks and Controls 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 […]