The biggest news this week was Google’s Agent2Agent (A2A) protocol, a new system that lets AI agents "talk" to each other. Think of it like teaching robots to speak the same language so they can team up on tasks. For example, a hotel’s reservation AI could automatically ask the billing AI to charge a customer’s card, then alert the cleaning robot—all without human help. Over 50 major companies, from Deloitte to SAP, are testing this tool to build smarter business systems.

Capgemini, a global tech consultancy, revealed plans to use Google’s A2A for industry-specific AI teams. In healthcare, this could mean a patient’s symptom-checking AI instantly shares data with a pharmacy AI to order medicine. For stores, inventory robots might tell delivery bots to restock popular items. This partnership highlights how agentic AI is moving from single tasks (like chatbots) to full problem-solving squads.

Why does this matter? Before A2A, companies often built AI tools that couldn’t share information—like having a team where no one speaks the same language. Google’s protocol acts as a universal translator, letting organizations mix AI from different brands. Vaclav Vincalek, a tech expert, compared it to teaching precise rules so agents don’t misunderstand each other.

Anthropic, another AI company, had earlier tried something similar called Model Context Protocol (MCP). But Google’s approach focuses on broader teamwork—not just connecting to databases. Early tests show A2A could cut project costs by letting companies reuse existing AI tools instead of building new ones from scratch.

Looking ahead, experts predict multi-agent AI will soon handle complex jobs like trip planning (booking flights, hotels, and rental cars automatically) or solving math problems by breaking them into smaller steps. The key challenge? Ensuring these agent teams stay secure and don’t share private data accidentally.