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Build Beyond Chat—
Make AI Do Sh*t.

No fluff. Just deep dives into tool-calling and agent auth to make your AI actually useful.

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PRODUCT RELEASE

Patterns for Agentic Tools: Your agents are only as good as your tools.

The Moment Every few years, a new pattern language emerges that changes how we build software. In 1994, the Gang of Four gave us Design Patterns. In 2003, Hohpe and Woolf gave us Enterprise Integration Patterns. Since then: Microservices Patterns, Cloud Patterns, and now Agent Patterns. But there's a gap. Agents can chat and reason on their own - but they can't ‘act’ without tools. Standards like MCP have unlocked how agents discover and call tools. The protocol layer is solved. What's missin

TUTORIALS

OpenClaw can do a lot, but it shouldn't have access to your tokens

OpenClaw (a.k.a. Moltbot, a.k.a. ClawdBot) went viral and became one of the most popular agentic harnesses in a matter of days. Peter Steinberger had a successful exit from PSPDFKit, and felt empty until the undeniable potential of AI sparked renewed motivation to build. And he's doing it it non-stop. OpenClaw approaches the idea of an Personal AI agent as a harness that communicates with you (or multiple users) in any of the supported channels in multiple sessions connected to the underlying

THOUGHT LEADERSHIP

Federation Over Embeddings: Let AI Agents Query Data Where It Lives

Before building vector infrastructure, consider federation: AI agents with tool access to your existing systems. For most enterprise use cases, that's all you need. Someone told you to pivot to AI. Add an AI layer. “We need to be AI-first.” Fair enough. So you start thinking: what does AI need? Data. Obviously. So the playbook writes itself: collect data in a central place, set up a vector database, do some chunking, build a RAG pipeline, maybe fine-tune a model. Then query it. Ship the chatb

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MCP

The MCP Gateway Pattern: scaling agentic integrations without tool sprawl

MCP makes it easy to go from “agent” to “agent that takes action.” The trap is that success compounds: every new system becomes a new server, every team ships “just one more tool,” and soon your integration surface is too large to reason about, too inconsistent to secure, and too messy to operate. Meanwhile, the model gets blamed for failure modes that are actually integration design problems. Tool definitions balloon. Selection accuracy drops. Context gets eaten before anyone types a prompt. A

How Arcade Proactively Addressed The First Major Identity Vulnerability in Agentic AI

While building an AI demo has become trivially easy, production-grade deployments in enterprises have been stifled by performance issues, costs, and security vulnerabilities that their teams have been warning about. Today, we're addressing one of those vulnerabilities head-on. A new class of identity attack Security researchers at The Chinese University of Hong Kong recently identified new variants of COAT (Cross-app OAuth Account Takeover), an identity phishing attack targeting agentic AI a

TUTORIALS

New Year, New Agents to Make You More Productive

Most conversations about AI agents still start the same way: models, prompts, frameworks, followed by an incredible looking demo. Then someone asks, “Okay… when can it ship to production?” That’s where things get a little awkward. The naked truth in the fading demo afterglow is that agents are apps. Which means they need identity, permissions, real integrations, and a way to behave predictably when something goes sideways. Without these components, any agent can dazzle a boardroom, but it won
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