Tag: multi-agent systems
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 ...
Google’s Scion Gives Developers a Smarter Way to Run AI Agents in Parallel
Tom Smith | | Agentic Workflows, AI agents, Container-Isolation, Developer-Tooling, Distributed-AI-Runtimes, Emergent-Coordination, Git-Worktrees, multi-agent orchestration, multi-agent systems, open source, Parallel-Execution, sandboxing, Scion-CLI, System-Process-AgentsGoogle's open-source Scion testbed lets developers run isolated, parallel AI agents across local and remote clusters. Here's how it works ...
Microsoft Field Engineers Built a Six-Agent Research Pipeline in VS Code That Fact-Checks Its Own Output
Tom Smith | | Agent Handoff Pattern, Azure Kubernetes Service (AKS), Azure Red Hat OpenShift (ARO), Fact-Checking AI, GitHub Copilot Agents, Global Black Belts (GBB), Grounding, MCP Server, multi-agent systems, Project Nighthawk, Technical Research Automation, VS Code Agent SkillsAzure Global Black Belts Diego Casati and Ray Kao developed Project Nighthawk, a multi-agent system that automates deep technical research for AKS and ARO with 100% source-grounding ...
Anthropic Code Review Dispatches Agent Teams to Catch the Bugs That Skim Reads Miss
Tom Smith | | AI code review, AI coding agents, Anthropic Code Review, Claude Code, Code Review Automation, developer productivity, devops, Engineering Velocity, GitHub integration, Logical Bug Detection, multi-agent systems, Pull Request Automation, secure software development, software quality, software reliabilityAnthropic Code Review dispatches agent teams to find bugs in PRs. Multi-agent analysis, severity ranking, and inline fixes for Teams and Enterprise ...
MCP-Powered Agentic AI in DevOps: Building Secure, Scalable Multi-Agent Pipelines for Autonomous SRE and Observability
Abhijit Ubale | | agentic AI, AI-Driven DevOps, autonomous intelligence, cloud-native infrastructure, devops evolution, MCP, Model Context Protocol, multi-agent systems, resilience and efficiency, site reliability engineeringDiscover how model context protocol (MCP) powered agentic AI is transforming DevOps by enhancing resilience and efficiency in cloud-native environments. Learn about the challenges, benefits, and real-world applications of autonomous multi-agent systems ...
Google Launches Agent Development Kit for TypeScript: A Code-First Approach to Building AI Agents
Tom Smith | | Agent Development Kit, AI agent orchestration, AI agents, AI development framework, autonomous AI agents, CI/CD AI agents, code-first AI, DevOps AI tools, Gemini Flash, Gemini Pro, Google Cloud Run AI, Google Gemini, JavaScript AI framework, multi-agent systems, open-source AI framework, TypeScript AI, TypeScript DevOps, Vertex AIGoogle’s open-source Agent Development Kit (ADK) lets TypeScript developers build modular, testable AI agents using familiar code-first workflows instead of prompt engineering ...
