Tag: MCP
env zero Previews Control Plane for Agentic DevOps Workflows
Mike Vizard | | agentic AI, AI agents, AI Governance, autonomous remediation, cloud infrastructure, CloudQuery, CMDB, devops, devops automation, env zero, EZ Control, IaC, infrastructure as code, infrastructure drift, kubernetes, MCP, platform engineeringenv zero’s EZ Control introduces an agentic DevOps control plane that detects infrastructure drift, applies policy-driven remediation and keeps automated changes attributable, reversible and auditable ...
Splunk Open Sources Token Meter Tool for Application Developers
Mike Vizard | | agent monitoring, agentic AI, AI budgets, AI coding agents, AI cost management, AI costs, AI Development, AI FinOps, AI observability, Claude Code, Codex, coding assistants, Cursor, developer productivity, developer tools, devops, Kiro, MCP, Model Context Protocol, model costs, OpenCode, Pi, Splunk, token consumption, Token Meter, token usageSplunk’s open-source Token Meter gives developers real-time visibility into AI coding agent activity, token consumption and estimated costs across tools including Claude Code, Codex and Cursor ...
Apica Adds AI Agents and MCP Server to Platform for Managing Telemetry Data
Apica today added an artificial intelligence (AI) agent and Model Context Protocol (MCP) server to version 3.0 of its Ascent platform for managing telemetry data pipelines. Andi Mann, chief product and technology ...
Harness Unfurls Source Code Repository Alternative to GitHub
Harness today launched a code repository service that is specifically designed for DevOps teams that are relying on artificial intelligence (AI) agents to generate code. Martin Reynolds, Field CTO for Harness, said ...
Waterfall 2.0: Controlling LLM-Driven Software Development with Stage-Gated Discipline
Georgy Starikov | | ADRs, Agile vs Waterfall, AI coding, AI development lifecycle, AI Software Engineering, AI-assisted engineering, architecture decision records, combi-shaper, engineering workflows, generative AI development, knowledge corpus, LLM engineering, LLM software development, MCP, Model Context Protocol, software architecture, software testing, system design, Waterfall 2.0Waterfall 2.0 reimagines classic stage-gated software development for the LLM era, combining fast AI generation with structured validation, cheap backtracking and a shared knowledge corpus ...
HackerOne Extends Platform Reach to Remediate Source Code Vulnerabilities
HackerOne has added a remediation capability to its H1 Platform that reduces the amount of time required to remediate validated vulnerabilities and other weaknesses affecting specific lines of source code. Nidhi Aggarwal, ...
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 ...
Black Duck Extends Scope and Reach of Code Scanning Tool
Black Duck has updated its Coverity static analysis code scanning tool to provide deeper integrations with artificial intelligence (AI) tools along with updates to its user interface that make it simpler to ...
ManageEngine OpManager Nexus Adds MCP, GenAI, and Autonomous AI, With AI Sovereignty Built-In
ManageEngine OpManager Nexus enhanced its newly launched hybrid observability platform with a range of powerful AIOps capabilities. OpManager Nexus was launched in April 2026 with the merger of two of ManageEngine’s flagship ...
SmartBear Tightens Integration Between AI Coding and Testing Tools
SmartBear this week revealed it is narrowing the divide between coding and testing by integrating its platform with artificial intelligence (AI) coding tools. Sheryl Koenigsberg, senior vice president of product marketing at ...
Why Every DevOps Engineer is Suddenly Learning MCP
Before they were everywhere, developers spent ages stitching systems together, one by one. APIs changed everything. MCP wants to do the same for AI. ...
Tenet’s ‘Agentjacking’ Attack Turns Sentry Errors Into Code Execution
AI coding agents can create a new code execution risk when they treat externally influenced error data as trusted guidance and have access to command line tools, according to new research from ...
