Browse Artificial Intelligence Blogs (51)

John Edward explains why AI agents that look great in demos often break down in production, and what teams need to engineer around the model to make agents reliable at scale.

Rob Bos shares a curated set of GitHub governance references he regularly sends to teams, covering enterprise platform baselines, GitHub Actions supply-chain controls, GitHub Advanced Security rollout and triage practices, and GitHub Copilot governance topics like premium requests, extension governance, and MCP-related security notes.

Hidde de Smet breaks down why spec-driven development can either reduce risk or quietly add “delivery debt”, especially in regulated environments. He outlines six common process cost centers, a three-lane model (full/light/no-spec), and a small set of KPIs to tell when structure is paying off.

John Edward explains how Azure’s “Agentic Agents” can support resilient cloud operations across migration planning, observability, and continuous optimization. The article focuses on turning telemetry into actionable guidance, reducing alert fatigue, improving root-cause analysis, and driving cost, performance, security, and sustainability improvements in Azure environments.

Randy Pagels shares three practical habits for getting more reliable results from GitHub Copilot: start with intent, ask for multiple options, and explicitly review assumptions before accepting generated code.

John Edward outlines an end-to-end approach for building a ServiceNow-focused AI assistant in Microsoft Copilot Studio, aimed at ticket auditing, SLA risk monitoring, and engineer performance reporting. The guide covers a layered architecture, ServiceNow REST API integration via Power Platform custom connectors, and practical security considerations for enterprise deployment.

John Edward explains how to combine a Copilot Studio agent with Power Automate to draft emails and route them through a human approval step before sending, creating a practical “human-in-the-loop” workflow for safer, more consistent business communication.

John Edward explains what an Agent Optimizer is in Azure AI Foundry Agent Service and why it matters for building reliable AI agents. The article breaks down how optimization works in practice—evaluating performance, refining instructions, improving workflows, and learning from feedback—to increase accuracy, efficiency, and user satisfaction.

Hidde de Smet explains how MCP’s Enterprise-Managed Authorization changes MCP authentication from per-server OAuth consent to policy-driven sign-in via an identity provider, with VS Code 1.123 preview support for Entra ID, Okta, and Auth0 and governance controls that pair with GitHub Copilot’s MCP registry policies.

Thomas Maurer explains what Azure Local Small Form Factor (SFF) is and why it matters for edge scenarios, then outlines an end-to-end deployment flow: provisioning a device as an Azure resource, installing the Azure Local OS, registering it with Azure Arc, and running container workloads with Docker and K3s.

John Edward explains what “agentic AI” means in the Microsoft ecosystem, focusing on how goal-driven agents plan tasks, call tools, and maintain memory. The article maps those concepts to Azure AI Foundry, Semantic Kernel, and Microsoft Graph, with concrete enterprise workflow examples.

DevClass reports on Vercel’s Ship event announcements, focusing on the new open source eve agent framework and enterprise controls like Passport for bringing AI-built apps and agents under centralized identity and policy, including OpenID Connect support for providers such as Okta and Microsoft Entra ID.

John Edward introduces the GitHub Copilot Desktop App and explains how it extends Copilot beyond the IDE into a standalone workspace for understanding repositories, planning work, and getting AI help across day-to-day development tasks.

John Edward explains why AI apps are moving from single “copilot” assistants to multi-agent systems, and how Semantic Kernel can be used to orchestrate specialized agents that collaborate via tools, memory, and coordination patterns—along with the practical engineering challenges this introduces.

John Edward shares a practical list of 25 project ideas for building custom AI agents with Microsoft Copilot Studio, ranging from personal productivity assistants to customer support, onboarding, IT help desk, and document review bots.

John Edward explains how Retrieval-Augmented Generation (RAG) works and how Azure AI Search fits into a production-ready RAG architecture, covering indexing, semantic and vector search, embeddings, chunking strategies, and practical steps to build a basic retrieval + generation workflow.

Hidde de Smet shows how to add fast local guardrails for Azure Terraform by running fmt, validate, tflint, Trivy, and terraform-docs on every git commit. The post includes a working pre-commit config, Azure-specific lint rules, and an MCP-based workflow to keep generated HCL current and policy-aligned.

John Edward outlines practical ALM and environment strategy guidance for Microsoft Copilot Studio, focusing on how to run copilots like enterprise applications with multi-environment setups, solution-based development, source control, CI/CD pipelines, configuration management, governance, and ongoing monitoring.

Hidde de Smet compares the GitHub Copilot App and the VS Code Agents Window, focusing on how each surface supports agent-first workflows: isolated sessions, worktrees, review/CI loops, and customization via MCP and instruction files. It includes a practical “which one should you use?” decision guide for day-to-day development vs delegated work.

DevClass rounds up Microsoft Build announcements that matter to developers, including new Windows sandboxing for AI agents (MXC), an Arm-based Surface RTX Spark Dev Box, GitHub Enterprise Local for connected or air-gapped environments, Azure Linux updates, and Microsoft-maintained Coreutils for Windows.

John Edward explains how to design multi-agent architectures in Microsoft Copilot Studio, using an orchestrator copilot plus specialized agents (IT, HR, sales, analytics). The article covers communication options (Power Automate, Dataverse, REST APIs), governance and security considerations, and practical scaling guidance like monitoring, shared knowledge sources, and independent versioning.

John Edward walks through creating a first AI agent in Microsoft Copilot Studio, from defining the agent and connecting knowledge sources to enabling generative answers, testing conversations, and publishing to channels like Teams and websites.

John Edward explains where Microsoft Copilot Studio sits in the Power Platform, and how it connects conversational AI to apps, workflows, data, and analytics through tools like Power Apps, Power Automate, Dataverse, and Power BI.

Jesse Houwing shows how to automate GitHub Copilot AI Credits budgeting by assigning per-user budgets based on Microsoft Entra ID group membership, using a GitHub Actions workflow and a PowerShell script that calls the GitHub enterprise billing API via the GitHub CLI.

Hidde de Smet lays out a practical KPI scorecard for teams adopting AI coding agents under usage-based billing, using GitHub Copilot’s AI Credits model as the concrete example. It focuses on measuring speed, quality, reliability, and spend together, with a rollout plan and data sources you can wire into a weekly dashboard.

Randy Pagels explains how to reduce repeated prompting by capturing team conventions in a copilot-instructions.md file so GitHub Copilot can generate code that matches your repo’s standards, architecture expectations, and preferred testing and design patterns.

John Edward explains how Declarative Agents and Autonomous Agents differ in Microsoft Copilot Studio, focusing on how each approach handles control, decision-making, and multi-step work. The article maps the trade-offs (predictability vs flexibility), gives practical use cases for each agent type, and offers guidance on choosing the right model for a given workflow.

John Edward explains how Microsoft Copilot Studio structures conversational agents using topics (intent-focused conversation pathways) and nodes (step-by-step actions inside a topic). The article breaks down common node types, shows how topics and nodes fit together in real flows, and shares practical design tips for building maintainable copilots.

Jesse Houwing breaks down why common “AI ROI” dashboards (tokens, PR counts, lines of code) don’t actually measure value, and how they can backfire through metric gaming and biased attribution. He proposes outcome-based measurement and post-build validation practices that better reflect real impact.

John Edward outlines common enterprise AI agent architecture patterns you can implement with Microsoft Copilot Studio, including single-agent designs, multi-agent orchestration, RAG, human-in-the-loop workflows, and event-driven automation, with notes on integrations, governance, and compliance considerations.

John Edward outlines practical Microsoft Copilot Studio scenarios teams are using to cut repetitive work, including customer support, HR onboarding, IT help desk triage, internal knowledge search, sales lead qualification, and meeting follow-ups across common Microsoft 365-connected workflows.

Harald Binkle demonstrates a practical BMAD workflow using GitHub Copilot to turn fuzzy requirements into reviewable artifacts: a PRD, project context, epics/stories, architecture decisions, risk-based test design, and traceability. The example focuses on enterprise authentication concerns like MFA, tenant isolation, RBAC, and auditability.

Rob Bos argues that as GitHub Copilot shifts to usage-based billing, teams should stop fixating on token costs in isolation and instead measure what they get back: foundation work, reduced tech debt, and faster MVP delivery. He shares real usage patterns, cost concentration among heavy users, and practical steps to manage spend without throttling engineers.

Hidde de Smet breaks down what AI coding agents actually cost once GitHub Copilot switches to usage-based billing, including how credits map to tokens, why model choice changes your bill, and how to budget for agent-heavy teams without surprising finance.

Rob Bos introduces the GitHub Copilot App technical preview and shares a practical first look at using it for repository maintenance, including parallel agent sessions, session modes (Interactive/Plan/Autopilot), and the Agent Merge workflow for handling CI failures, merge conflicts, and security-related alerts.

John Edward explains how GitHub Copilot changes team workflows around pull requests, code review expectations, and knowledge sharing. The article focuses on the trade-offs of faster AI-assisted coding, why review discipline matters more, and how teams can add guardrails like testing and security scanning without losing collaboration.

Jesse Houwing breaks down why GitHub Copilot is moving from Premium Request Units to token-based, usage-based billing, and what that means for model selection, cost predictability, and newer features like Agent Mode, Cloud Coding Agent, and Copilot Code Review—especially for organizations managing budgets and policies.

John Edward outlines an architecture for a “Daily Stand-Up Agent”: a custom AI copilot that pulls sprint activity from Jira and Azure DevOps, detects blockers, and generates consistent stand-up summaries. The post focuses on connectors, grounding ticket data, conversational reporting, and practical considerations like security and data quality.

Rob Bos shares a real-world GitHub Copilot CLI mishap where an unintended Copilot CLI extension caused repeated prompts to close GitHub deployment-status notifications, and explains how he tracked down the source and removed it.

DevClass reports on the Zed editor reaching version 1.0, covering its Rust-based architecture, GPU-accelerated UI, built-in language server support, and the editor’s growing set of AI features (including agents) alongside an option to disable AI entirely.