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 in order to move from fundamentals to deeper implementation without losing the thread.
Claw Code's public docs and parity repo reveal how an AI coding agent is assembled: runtime loop, tools, permissions, MCP connections, sessions, and plugins.
A practical Claw Code usage guide for turning agent architecture into safe daily workflows with bounded tasks, permission gates, tests, checkpoints, and rollback.
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
Use these goals to move through the topic with a clear sense of what each section should help you understand.
Archive
Topic hubs sit between categories and tags: more curated than one label, less rigid than a formal guide.
DeepSeek Harness is more than another model release. Here is what its open-source, plugin-first agent runtime means for developers building real coding workflows.
A practical Claw Code usage guide for turning agent architecture into safe daily workflows with bounded tasks, permission gates, tests, checkpoints, and rollback.
Claw Code's public docs and parity repo reveal how an AI coding agent is assembled: runtime loop, tools, permissions, MCP connections, sessions, and plugins.
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.
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.
Compare AI agent tools for coding, task automation, and personal workflows, with a decision framework for Cursor, Claude Code, Copilot, Operator, Manus, Cowork, and Clawdbot.
Learn how Model Context Protocol connects AI agents to tools and data, with a practical overview of hosts, clients, servers, permissions, security controls, and implementation...
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...
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.
OpenAI's GPT-OSS brings powerful AI to laptops and phones. Complete guide to hardware requirements, open-source ecosystem, privacy benefits, and setup.
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.