Tag: AI coding agents
GitHub’s Spec Kit Puts the Spec Back in Software Development
Tom Smith | | AI coding agents, devops automation, GitHub Copilot, GitHub Spec Kit, software governance, Spec-Driven Development (SDD), technical debt, vibe codingGitHub’s open-source Spec Kit aims to eliminate "vibe coding" by prioritizing durable specifications over vague prompts, providing a structured, agent-agnostic workflow for Copilot, Claude, and Gemini ...
The Great Decoupling: Scaling the Outer Loop for the Agentic Era
Nassir Khan | | AI coding agents, AI-SDLC, compliance automation, devsecops, software development lifecycleThe "Inner Loop" of software development—the iterative cycle of writing, building, and debugging code—has just broken the sound barrier. With the emergence of agentic coding tools like Claude Code and GitHub Copilot ...
Cursor’s New SDK Turns AI Coding Agents Into Deployable Infrastructure
Tom Smith | | agent control plane, AI coding agents, autonomous pull requests., CI/CD agent pipelines, Cloud execution sandboxing, Composer 2 model, Cursor SDK, devops automation, MCP integration, Model Context Protocol, Multi-agent workflows, Programmatic code generation, semantic search indexing, software lifecycle engineering, TypeScript SDK for AICursor's TypeScript SDK lets teams invoke AI coding agents programmatically from CI/CD pipelines, with sandboxed VMs, MCP support, and token-based pricing ...
5 Facts About AI Coding Agents from Comprehensive Benchmarking
Graham Neubig | | AI coding agents, benchmarking, developer tools, Large Language Models, software developmentAI coding agents are becoming more capable, but evaluating them is harder than it looks. Most benchmarks focus on a single dimension of agent capabilities; for instance, the popular SWE-Bench benchmark only ...
If it Isn’t Code, it’s Just Advice
David Mytton | | AI coding agents, API contracts, audit trails, automated verification, automation vs. advice, CI/CD, code-native, configuration as management, dashboard problem, infrastructure as code, MCP Server, production safety, reproducibility, software development life cycle, software security, spam prevention, UI-driven configuration, unit testing, vendor integrations, version controlIn the world of AI coding, success hinges on code-native solutions that integrate and verify changes directly in the codebase. Many AI tools fail by relying on external dashboards, lacking the reproducibility ...
Meta Researchers Show AI Agents Can Verify Code Without Running It — and Hit 93% Accuracy
Tom Smith | | Agentic Code Reasoning, AI coding agents, Code Equivalence Verification, Defects4J Benchmark, DevOps Code Review, Execution-Free Reward Signals, Meta Research, Reinforcement Learning Training, RubberDuckBench, Semi-Formal Reasoning, software lifecycle engineering, static analysisMeta's semi-formal reasoning enables AI agents to verify code without executing it, achieving 93% accuracy. Implications for code review and RL training costs ...
GitHub Adds 37 New Secret Detectors in March, Extends Scanning to AI Coding Agents
Tom Smith | | AI coding agents, automated remediation, Credential Leakage, DeepSeek, devops security, GitHub MCP Server, GitHub Secret Scanning, LangChain, Push Protection, secret detection, Snowflake, Software Supply Chain, Token Validity Checks, VercelGitHub's March 2026 updates introduce secret scanning for AI agents via MCP, 37 new detectors, and expanded push protection. Learn how to secure AI-generated code ...
Gemini Code Assist Gets Agent Auto-Approve, Inline Diffs, and Custom Commands to Speed Up the Core Coding Loop
Tom Smith | | Agent Mode, AI coding agents, AI-assisted development, Auto Approve, code review, Context Drawer, custom commands, developer productivity, devops, Finish Changes, Gemini Code Assist, Google Gemini, inline diff, IntelliJ, Outlines, software lifecycle, VS CodeGoogle’s latest Gemini Code Assist updates focus on workflow, not hype: Agent Mode with Auto Approve, inline diffs, a Context Drawer, custom commands, Finish Changes, and Outlines streamline how developers review, trust, ...
Microsoft Azure Skills Plugin Gives AI Coding Agents a Playbook for Cloud Deployment
Tom Smith | | agent orchestration, AI coding agents, AI in software delivery, Azure automation, Azure cost optimization, Azure Skills Plugin, Claude Code, cloud deployment, devops, Foundry MCP, GitHub Copilot, guardrails, infrastructure as code, institutional knowledge, MCP Server, Microsoft Azure, platform engineering, VS Code agent pluginsMicrosoft’s new Azure Skills Plugin closes the gap between AI-written code and production deployments by packaging expert Azure knowledge as executable skills, backed by MCP servers for real infrastructure actions and AI ...
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 ...
Claude Code Remote Control Keeps Your Agent Local and Puts it in Your Pocket
Tom Smith | | AI coding agents, AI developer workflows, Anthropic AI coding, DevOps AI tooling, local vs cloud AI agents, local-first AI development, remote AI supervision, secure AI code executionClaude Code Remote Control lets developers run AI coding agents locally while supervising them from any browser or phone. Anthropic’s local-first approach contrasts with cloud agents and reshapes how teams govern AI-driven ...
Cursor Cloud Agents Get Their Own Computers — and 35% of Internal PRs to Prove It
Tom Smith | | agent-driven development, AI code review, AI coding agents, autonomous software development, CI/CD and AI agents, cloud-based AI development, Cursor cloud agents, devops automation, self-verifying AI code, software engineering automationCursor’s new cloud AI coding agents can build, test, and verify software autonomously using real UI interaction. With 35% of production PRs generated by agents, software delivery is shifting from code authoring ...
