Topic hub
A practical path for understanding coding agent runtime design, tool systems, MCP integration, permissions, sessions, and extensibility.
Overview
Study how serious AI coding agents are put together beyond the model layer. This topic hub focuses on the architecture patterns that make coding agents usable in real developer workflows: runtime loops, tool execution, permission models, MCP-powered external capabilities, session continuity, hooks, plugins, and migration discipline. It is designed for readers who want to understand the operating environment around AI coding agents rather than only compare model demos or benchmark charts.
Start here
Follow these featured reads to move from architecture basics to migration discipline without losing the thread.
Claw Code's public docs and parity repo offer a useful blueprint for how modern AI coding agents are actually structured beyond the model layer.
Claw Code's parity repo shows why modern coding agents often split responsibilities between Rust for runtime-critical paths and Python for orchestration and migration.
A coding model becomes a real agent only when tool execution, permission policy, and MCP integration are designed as one coherent system.
Hooks, plugin registries, and persistent sessions are what turn an AI coding assistant into an extensible platform instead of a one-shot demo.
Claw Code's parity workflow offers a strong model for teams rebuilding or migrating complex agent systems without drifting into vague rewrites or cargo-cult copies.
Learning goals
This hub is designed for developers who want to understand how coding agents work as systems, not just as model demos.
Archive
Topic hubs sit between categories and tags: more curated than one label, less rigid than a formal guide.
Claw Code's public docs and parity repo offer a useful blueprint for how modern AI coding agents are actually structured beyond the model layer.
Claw Code's parity workflow offers a strong model for teams rebuilding or migrating complex agent systems without drifting into vague rewrites or cargo-cult copies.
Hooks, plugin registries, and persistent sessions are what turn an AI coding assistant into an extensible platform instead of a one-shot demo.
Claw Code's parity repo shows why modern coding agents often split responsibilities between Rust for runtime-critical paths and Python for orchestration and migration.
A coding model becomes a real agent only when tool execution, permission policy, and MCP integration are designed as one coherent system.
Announcements from NVIDIA and Anthropic between March 16 and March 17, 2026 show the AI race shifting toward long-running agents, policy runtimes, and physical-world execution.
Microsoft's Agent 365 points to a new enterprise AI battleground: a control plane for agent identity, policy, observability, and governed tool access.
Google's March 2026 Pixel Drop suggests Gemini is moving beyond chat and becoming a cross-app action layer for mobile, which may matter more than another model benchmark.
On March 6, 2026, Anthropic and Mozilla said Claude-assisted research helped uncover 22 Firefox CVEs, signaling AI's move into real security work.
OpenAI's March 2026 GPT-5.4, Codex Windows, and Codex Security releases point to a bigger shift: the company is building a full agent stack for professional work.
The definitive 2026 comparison of large language model programming abilities. Real benchmark data from SWE-bench, LiveCodeBench, and developer testing reveals which AI codes best.
The definitive comparison of 2026's hottest AI agents—coding assistants, task automators, and personal AI butlers. Find your perfect agent stack.
Model Context Protocol (MCP) is the universal standard for connecting AI agents to external tools and data. Learn how to build MCP servers and clients with practical code examples.
Apple's M5 chip delivers 4x GPU performance boost with enhanced Neural Engine. Discover how this breakthrough transforms AI development workflows for programmers.
METR study reveals AI coding assistants increase task completion time by 19% despite making developers feel faster. Discover the productivity paradox and how to use AI tools...
Anthropic enables real-time Python and Node.js execution in Claude AI. Game-changing developer tool compresses feedback loops with new security challenges.
AI agents evolved from simple Q&A to autonomous workers. Discover why this transformation matters and how it will reshape work and productivity in 2025.
From demo to deployment: real lessons building production AI agents. Learn circuit breakers, token management, and cost optimization to avoid API disasters.
Top 7 AI development trends shaping 2025: multi-modal systems, cost-effective deployment, edge computing, and strategies every developer should master now.
GPT-5 transforms programming. Real-world benchmarks, pricing breakdown, common pitfalls, and 5 expert tips to boost your coding productivity with AI tools.
Agentic AI emerges as 2025's hottest trend with 90% developer adoption. Discover how autonomous AI agents are transforming workflows and what it means for your business.