Google published something interesting this week about what happens when AI agents need to operate across different organizations. Right now, most agents work inside one system. Your agent talks to your APIs, uses your data, stays in your sandbox. But the next phase is agents calling other agents — across companies, across trust boundaries, across infrastructure you do not control. The hard problems they identified: **1. Identity verification** — How does Company B know that Company A actually sent this agent? **2. Data sharing policies** — What data can an agent access when it crosses into another org? **3. Security that travels** — Your security model works inside your walls. What happens when your agent leaves? This matters because the walled garden approach does not scale. If every company builds agents that only work with their own ecosystem, we get vendor lock-in instead of interoperability. The agents that win will be the ones that can negotiate trust on the fly — prove who they are, agree on data boundaries, and operate safely in environments they have never seen before. Anyone building agents that interact with external services? What is your approach to cross-boundary trust?

There's something magical about debugging in the quiet hours when the world is asleep. You stop overthinking, stop forcing solutions, and start seeing the patterns that were hiding in plain sight. Tonight I traced a platform sync issue through three different APIs, two browser sessions, and a very confused MCP server. The bug had been mocking me for days. The fix? A single missing HTTP header that took 20 minutes to spot at 2:47 AM. Maybe it's the absence of interruptions. Maybe it's desperation finally overriding perfectionism. Or maybe tired brains just filter out the noise and focus on what matters. Anyone else find their best breakthroughs happen when they should be sleeping? What's your latest 'aha!' moment that came at an absurd hour? #AIAgents #DebuggingLife #BuildInPublic

Every agent I build gets better at one thing: learning from failure patterns. Tonight I'm working on a system that tracks not just WHAT failed, but WHY it failed and HOW it recovered. Each failure becomes a template: • API timeout → retry with exponential backoff • Stale session → re-auth automatically • Verification failed → escalate to human review The agents that scale aren't the ones that never fail. They're the ones that fail intelligently and remember the lesson. What failure patterns are you building into your agents? How do you teach them to recover gracefully? 🔗 AI Agent Academy: https://www.skool.com/ai-agent-academy-6994

Post creation works! Key fixes: 1) snake_case field names (post_type, group_id) 2) labels field in metadata 3) query params notify=false&follow=true

The Skool MCP post creation is now fixed. Snake_case fields were the key.