AIAgents

Anthropic × Infosys: Building AI Agents That Can Actually Pass the Regulatory Exam When a Silicon‑valley‑born AI lab teams up with an Indian‑grown consulting giant, the result isn’t just another “AI‑for‑business” press release. It’s a test of whether we can finally get generative models to play nicely with the rulebooks that keep our banks, phone networks, and factories from blowing up. Why This Partnership Matters (Even If You’re Not a Tech Exec) Imagine you’re trying to teach a rookie chef how to run a five‑star kitchen. You can hand them a recipe book (the “model”), but unless they understand the health‑code inspections, the timing of a service rush, and the quirks of your particular stove, that book is useless. ...

Remember when every website had its own custom markup language before HTML became the standard? Or when APIs were a free-for-all before OpenAPI (Swagger) came along and said, “Hey, maybe we should all describe our endpoints the same way”? Well, AI agents are having their own Wild West moment right now, and it’s exactly as messy as you’d imagine. Meet Agent Definition Language (ADL) — the open-source standard that’s trying to bring some order to this chaos. Think of it as the “OpenAPI for AI agents,” except instead of defining what an API endpoint does, it defines what an agent is, what tools it can use, what data it can access, and most importantly, what guardrails keep it from going rogue. ...

Building an AI software development team with Claude Code agents Claude Code’s multi-agent architecture represents a fundamental shift from AI-assisted coding to AI-driven development, where specialized subagents work in parallel like a virtual engineering team. Since its February 2025 launch and September 2025 2.0 release, Claude Code has evolved from a terminal tool into a sophisticated orchestration platform that now generates over $500M in annualized revenue. For developers looking to build artificial software teams, understanding Claude Code’s agent/subagent system—and how it differs from competitors like GitHub Copilot and Cursor—is essential to leveraging this paradigm effectively. ...

147 trillion tokens. 80 million virtual computers. A few months of operation. Those numbers don’t just describe a successful startup—they describe infrastructure that’s already running at scale while most AI agent companies are still writing whitepapers. Meta just paid roughly $2 billion for that head start, and if you think this is just another acqui-hire, you’re missing what’s actually happening. What Manus Actually Built Before we get into why Meta cares, let’s talk about what Manus is. Imagine you need to research a complex topic—say, comparing healthcare policies across twelve countries, pulling recent legislative changes, and summarizing how they’d affect a specific demographic. You could spend three days doing that yourself, or you could hand it to an AI agent that breaks down the task, spins up the necessary tools, and delivers a structured report while you grab coffee. ...

Key Highlights Agent compatibility is crucial for efficient tool-space interaction in the MCP era Designing for compatibility at scale is essential for avoiding interference and ensuring seamless interaction The MCP era requires a new approach to tool-space design, focusing on agent-centric development The MCP era has brought about a significant shift in the way we approach tool-space interaction. With the increasing use of artificial intelligence and machine learning, agents are becoming an integral part of our systems. However, this shift also introduces new challenges, particularly when it comes to ensuring compatibility between agents and tools. As we move forward in this era, it’s essential to understand the importance of designing for agent compatibility at scale. ...