Topic Hub
AI coding agents represent the next shift in software development — systems that don't just complete code but take multi-step actions: running tests, opening pull requests, fixing failing CI, and iterating without waiting for human prompts. These articles cover the architecture, security implications, enterprise deployment patterns, and the competitive landscape of autonomous coding tools.
How to build a production MCP server: a hands-on guide to JSON-RPC, the Streamable HTTP transport, tools, and discovery — from one I shipped on Cloudflare.
At GTC 2026, Jensen Huang said every company needs an OpenClaw strategy. Here is what it means and what U.S. teams should do next.
How Claude Code Review works: multi-agent PR reviewer, pricing, REVIEW.md customization, and where it beats static analyzers. Complete guide for 2026.
Agentic AI breaks static enterprise security models. How to fix identity, delegated authority, prompt injection defense, and tool-level policy in 2026.
GPT-5.4 benchmarks, use cases, pricing, API, long-context behavior, and GPT-5.4 Pro comparison. The complete guide for developers and power users.
AI coding agents are AI systems that autonomously write, review, test, and deploy code by taking multi-step actions in a development environment. Unlike simple code completion, they run commands, read documentation, make pull requests, and iterate based on test results or feedback — operating with a degree of autonomy rather than completing a single prompt.
AGENTS.md is a project-level instruction file — similar in concept to a README — that tells AI coding agents how to work within a specific repository. It documents coding conventions, forbidden operations, context about the codebase, and agent-specific instructions. Well-written AGENTS.md files reduce agent errors and produce more consistent results without requiring explicit prompting each session.
AI coding agents face several enterprise security risks: prompt injection (where malicious content in code, documentation, or issues hijacks agent actions), over-permissioned tool access, supply chain attacks via crafted dependencies, and data exfiltration through agent outputs. Secure deployments require sandboxing agent execution, scoping tool permissions, and requiring human approval for sensitive operations like deployments or secrets access.
Anthropic, OpenAI, and NVIDIA are each building distinct agent platforms. Anthropic focuses on Claude Code for developer workflows and the Model Context Protocol (MCP) for tool standardization. OpenAI is embedding agent capabilities directly into GPT-5 models. NVIDIA's OpenClaw targets enterprise agentic orchestration at scale. The Model Context Protocol is emerging as a cross-vendor standard for connecting agents to tools and data sources.