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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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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

THOUGHT LEADERSHIP

5 Takeaways from the 2026 State of AI Agents Report

AI agents have moved quickly from experimentation to real-world deployment. Over the past year, organizations have gone from asking whether agents work to figuring out how to deploy enterprise AI agents reliably at scale. The 2026 State of AI Agents Report from the Claude team captures this shift clearly. Drawing on insights from teams building with modern LLM agents—including those powered by models from providers like Anthropic—the report offers a grounded view of how agentic systems are bein

THOUGHT LEADERSHIP

What It’s Actually Like to Use Docker Sandboxes with Claude Code

We spend a lot of time thinking about how to safely give AI agents access to real systems. Some of that is personal curiosity, and some of it comes from the work we do at Arcade building agent infrastructure—especially the parts that tend to break once you move past toy demos. So when Docker released Docker Sandboxes, which let AI coding agents run inside an isolated container instead of directly on your laptop, we wanted to try it for real. Not as a demo, but on an actual codebase, doing the k
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