The Rise of Agent Experience (AX)

Introducing AX: Why Agent Experience Matters

As builders, we need to start focusing on AX or “agent experience” — the holistic experience AI Agents will have as the user of a product or platform.

Agent Plugins

A portable package format for reusable components that extend AI agents.

A tale of two Agent Builders

What two competing solutions to the same design problem tell about the future of designing AI interfaces.

Project Think: building the next generation of AI agents on Cloudflare

Announcing a preview of the next edition of the Agents SDK — from lightweight primitives to a batteries-included platform for AI agents that think, act, and persist.

AI Agents Will Become the New UI, and Apps Take a Backseat

For decades, screens, keyboards, and structured applications have shaped our relationship with technology. People learned how to interact with computers

Agent experience: How to design products that agents can actually use — WorkOS

What engineers and founders need to know about designing APIs, tools, and interfaces for agent-driven workflows

Dive into Claude Code: The Design Space of Today's and Future AI Agent Systems

Claude Code is an agentic coding tool that can run shell commands, edit files, and call external services on behalf of the user. This study describes its comprehensive architecture by analyzing the publicly available TypeScript source code and further comparing it with OpenClaw, an independent open-source AI agent system that answers many of the same design questions from a different deployment context. Our analysis identifies five human values, philosophies, and needs that motivate the architecture (human decision authority, safety and security, reliable execution, capability amplification, and contextual adaptability) and traces them through thirteen design principles to specific implementation choices. The core of the system is a simple while-loop that calls the model, runs tools, and repeats. Most of the code, however, lives in the systems around this loop: a permission system with seven modes and an ML-based classifier, a five-layer compaction pipeline for context management, four extensibility mechanisms (MCP, plugins, skills, and hooks), a subagent delegation mechanism with worktree isolation, and append-oriented session storage. A comparison with OpenClaw, a multi-channel personal assistant gateway, shows that the same recurring design questions produce different architectural answers when the deployment context changes: from per-action safety classification to perimeter-level access control, from a single CLI loop to an embedded runtime within a gateway control plane, and from context-window extensions to gateway-wide capability registration. We finally identify six open design directions for future agent systems, grounded in recent empirical, architectural, and policy literature.

10 things I learned from burning myself out with AI coding agents

Opinion: As software power tools, AI agents may make people busier than ever before.

How AI Agents Are Changing API Architecture | daily.dev

Autonomous AI agents are reshaping API consumption patterns, prompting API providers to rethink identity, authorization, security, design, and observability....

Meet Foundry: An AI Startup that Builds, Evaluates, and Improves AI Agents

Beyond agentic coding

AI dev tooling can do better than chat interfaces

Supermemory

The memory layer for AI agents. Context engineering platform powering enterprise APIs, developer plugins, and a personal app that remembers everything.

LukeW | Agent Management Interface Patterns

As an increasing number of AI applications evolve to agents doing work for people, agent management becomes a critical part of these product's design. How can p...