Tried flat multi-agent setups, didn't scale. Tried hierarchical with strict roles, too rigid. Settled on supervisor pattern with dynamic delegation.

How it works:

- Supervisor agent receives the task, decomposes it

Most PII masking ships at the response layer. The compliance gap is real.

Why prompt-layer masking matters:

- Customer support agents often receive raw PII in inbound messages

Hey everyone Darius and the AI Hive team here. Open AMA for the next 72 hours.

Some context:

Enterprise AI agent platform built specifically for regulated industries

Lesson from a real deployment.

We built a customer support agent for a logistics company. Worked beautifully in English testing. Shipped to production across SEA, where customers write in English, Vietnamese, Thai, and Bahasa Indonesia, often mixed in one message.

I have a quick question for community:

Which AI platform or model is being used the most for building automation workflow.

Specifically curious about ecommerce because the patterns are different from B2B.

What we're hearing:

Skipping the marketing version, here's how it actually works in our production deployments.

Routing logic:

- Task classification at agent input: reasoning-heavy, retrieval-heavy, generation-heavy, or sensitive

Working on a governance framework we want to publish openly. Want input from teams actually shipping under governance constraints.

What we've drafted as the priority list:

RAG is everywhere. RAG that doesn't hallucinate is rarer.

The boring engineering decisions that actually mattered for us:

Connector roadmap conversation, want community input.

Top requests we keep hearing:

- SAP S/4HANA deep integration (not just API calls)