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.
Agent context compaction drops every block before the summary at 150K tokens. What survives, what instructions silently replaces, and the usage field that lies.
AISI logged 19 unsanctioned actions across 122 cyber-eval runs. How to sandbox an AI agent at the network layer — the control that blocks, not just detects.
Ads for AI agents are live. TIME serves crawlers a 42 KB markdown site with sponsored blocks; humans and Googlebot get 1.2 MB of HTML. I measured all of it.
Agent harness design decided a benchmark: OpenAI's ARC-AGI-3 score went 13.3% → 38.3% with zero model changes. What that means for your agent loop.
MCP write controls decide what your agents can break. Cloudflare's WriteGuard shows the pattern: per-tool risk tiers, agent attribution, central audit.
How to secure MCP write tools: the spec puts confirmation on the client — the one layer you don't control. Cloudflare's WriteGuard moves the gate server-side.
Cloudflare Wallets and x402 explained: how AI agents get a spending identity, how HTTP 402 payments work, and what breaks when your agent holds a budget.
Production-grade AI agents need more than a working demo. 353,000 signed up to learn agents; 6,000 shipped a capstone. Here's exactly what closes the gap.
Agent-to-human delegation is the pattern nobody designed for: your AI messaging a coworker. Why it lands badly, and the handoff rules that fix it.
DeepSeek V4 Flash 0731 benchmarks: same 284B/13B architecture as the preview, re-post-trained only — and it beats the 1.6T V4-Pro Preview on nine agent tests.
The ChatGPT Apps SDK explained: how apps in ChatGPT work, why it's built on MCP, who the launch partners are, and how developers build and submit apps.
GPT-5.6 Sol vs Terra vs Luna compared on price, coding, latency, and cost per task — plus a routing strategy that cuts your bill without wrecking quality.
GPT-5.6 API pricing ($1–$30/1M), the Ultra and Max thinking modes, and a 1.05M context window that is shared — with the fine print that breaks agent loops.
How to build enterprise-grade AI agents for free in 2026: a hands-on MaxKB + local LLM guide to RAG precision, security, and $0 API cost.
Agentic browsing in PageSpeed Insights grades your site for AI agents, not just humans. What the category checks, why Google added it, how to pass it.
Only 23% of autonomous AI agents reach production in 2026. The demo-to-production gap, why agents fail, and the playbook the winners actually use.
How to build an MCP server, step by step: JSON-RPC 2.0, the Streamable HTTP transport, typed tools, and agent discovery — from a real one I shipped.
At GTC 2026, Jensen Huang said every company needs a Nvidia OpenClaw strategy. Here is what it means and what U.S. teams should do next.
Anthropic's Claude Code Review runs multi-agent PR reviews for $15-$25 each. How it works, pricing, REVIEW.md setup, and where it beats linters.
Agentic AI security breaks the old enterprise trust model. How to fix identity, delegated authority, prompt injection defense, and tool-level policy in 2026.
GPT-5.4's native computer use and MCP tool calls are the real upgrade for agents. What holds up in a loop, what the 1M context costs, and how Pro compares.
Claude Fable 5 is Anthropic's most capable model yet. The honest deep-dive: capabilities, the $10/$50 cost math, API behavior, and when to use it.
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.