Tag: AI agents
How to Build a Durable Change-Control Gate for AI Agents
Daniel Martin | | agentic AI, AI agents, AI automation, AI Governance, AI safety, approval workflows, change control, devops, durable workflows, human-in-the-loop, idempotency, LangGraph, NIST AI RMF, policy enforcement, production deployment, release controls, tool-using agents, workflow governanceAI agents that trigger real-world changes need more than confidence scores. A durable change-control gate should recheck policy, require approval for consequential actions, enforce idempotency and verify the result before retrying ...
The Missing Runtime for Long-Running AI Agents
Anuj Kapoor | | agent governance, agent orchestration, agent runtime, AI agents, AI observability, AI resiliency, Azure AI Foundry, Azure Durable Functions, devops, distributed systems, durable AI agents, durable workflows, Enterprise AI, human-in-the-loop, long-running AI agents, multi-agent systems, platform engineering, stateful AI, workflow orchestrationEnterprise AI agents need more than stronger models. They need durable execution environments that can coordinate multi-step workflows, survive failures, pause for human review and resume reliably after disconnects or delays. AI ...
Preparing Infrastructure for the Next Phase of Agentic AI
Gretchen Stewart | | agent orchestration, agentic AI, AI agents, AI data architecture, AI infrastructure, AI modernization, AI observability, AI resource management, AI workflows, AI workload management, autonomous agents, cloud infrastructure, data movement, federal agencies, government AI, government IT, human-in-the-loop, hybrid infrastructure, infrastructure modernization, multi-agent systems, on-premises AI, public sector AIAgentic AI is changing government infrastructure requirements, pushing agencies to rethink workflows, observability, data movement and resource prioritization before investing in new hardware ...
Claude Code’s Temporary Usage Boost Expires Tonight. Here’s What Actually Changes
Tom Smith | | AI agents, AI coding agents, AI coding costs, AI coding tools, AI subscription pricing, Anthropic, CI pipelines, Claude Code, Claude Code usage limits, developer productivity, devops, Fable 5, software development, token limits, weekly usage capsAnthropic’s temporary 50% increase to Claude Code weekly usage limits expires August 19, reducing available capacity for developers and DevOps teams without lowering subscription prices ...
Are LLMs Equally Good (or Bad) at Building Secure Software?
Pieter Danhieux | | agentic AI, AI agents, AI code security, AI coding assistants, AI coding costs, AI coding models, AI Development, AI model benchmarking, AI-generated code, application security, Developer Security, framework security, LLM security, OWASP Top 10, SAST, secure code generation, secure coding, software development, software security, token costsAn SCW study finds a large variance in how frontier and budget LLMs perform across different frameworks, and explains the striking cost variance ...
What the Microservices Era Can Teach Us About AI
Mark Fussell | | agent identity, agent observability, agent runtime, agentic AI, agentic durable execution, AI agents, AI architecture, AI Governance, AI infrastructure, AI security, autonomous agents, cloud-native AI, distributed systems, durable execution, LLM observability, microservices, Open Standards, platform engineering, vendor lock-in, workflow orchestrationAI agents are not just microservices with LLMs attached. Their long-running, non-deterministic workflows demand durable execution, per-step identity, governance and observability ...
GitHub Copilot’s Latest Update Bets on Model Choice, Not Model Loyalty
Tom Smith | | Agent Plugins 1.0, AI agents, AI coding tools, Claude model switching, Copilot CLI, Copilot Memory, Copilot updates, developer tools, devops, GitHub Copilot, GitHub Copilot models, Kimi K3, MAI-Code-1.1-Flash, Ollama, platform engineering, VS Code 1.133GitHub’s latest Copilot updates add Kimi K3, MAI-Code-1.1-Flash, Agent Plugins 1.0, model switching, CLI improvements, and local Ollama support ...
Developer Resistance to AI Isn’t Fear – It is Identity
Lukasz Lazewski | | 1000x developer, agentic coding, AI adoption, AI agents, AI code review, AI coding tools, AI Orchestration, AI transformation, AI-assisted coding, AI-generated code, coding with AI, developer accountability, developer experience, developer identity, developer motivation, developer productivity, developer resistance to AI, developer trust in AI, developer workflow, Engineering leadership, Future of Programming, human-AI collaboration, software architecture, software developers, software engineeringDeveloper resistance to AI is less about job loss than a deeper shift from hands-on coding to supervising, validating and orchestrating AI-generated work ...
Why Reliability Guardrails Are Needed in Every AI Coding Pipeline
Kolton Andrus | | ai, AI agents, chaos engineering, CI/CD, reliability, site reliability engineeringWe’re in the middle of a reliability reckoning. Thanks to AI, companies are shipping code much faster than before. But if there’s anything to learn from the surge in high-profile outages over ...
From API Integration to Agent Governance: What Backend Teams Need to Know About MCP
Many MCP projects begin with an existing API and a simple request: expose one backend capability to an LLM client. The quickest route is to wrap an endpoint as a tool and ...
Microsoft Confirms Copilot ‘Super App’ Is Coming This Year — and It’s About More Than Convenience
Tom Smith | | Agent 365, agent sprawl, AI agents, AI identity management, autonomous agents, Copilot Chat, Copilot Code, Copilot consolidation, Copilot Cowork, enterprise AI governance, GitHub Copilot, Microsoft 365 Copilot, Microsoft Autopilots, Microsoft Copilot super app, Microsoft Entra Agent ID, One CopilotMicrosoft is combining Copilot Chat, Code, Cowork and Autopilots into one super app, raising new questions about agent governance, identity, licensing and security ...
Why You Need AI Agent Security Validation in Software Testing
Engineering teams have been racing for the last two years to deploy AI agents that can find bugs faster than any QA team ever could. Autonomous testing agents can crawl through codebases, ...
