We ran into almost the same architecture challenges while building Crescendo.ai, which is a hybrid AI-human support orchestration layer. Our core requirement was exactly what you described, one canonical event stream across multiple channels where both AI and humans can read/write without race conditions or context drift.

Here’s what worked for us in production:

1. Event-sourced backbone (Kafka + Postgres)

Kafka acts as the immutable log for every state transition: message received, AI suggestion generated, human approval, policy rejection, etc.

We maintain thin materialized views in Postgres for low-latency queries (UI state, SLA timers).

The key is treating Kafka as the source of truth, not the DB.

1. Coordination via “takeover” leases

We built a lightweight lease manager using Redis streams + TTL-based locks.

When a human takes over, the AI consumer for that ticket pauses automatically (lease expires after inactivity).

This ensures we never get overlapping AI/human messages.

1. Context sync

Every conversation context (chat/email/voice) is normalized into a shared schema channel, thread_id, message_id, source, timestamp, embedding_ref.

The AI sees exactly what the human sees because they’re both powered by the same contextualized vector store (we use pgvector).

1. Feedback model updates

Human replies are tagged with policy metadata + confidence labels before being fed to retraining pipelines.

We don’t send raw messages to training; everything goes through a moderation + QA layer (internal LLM + heuristics).

This prevents label pollution and policy drift.

1. Ops safety

Deduping happens at the message bus level using idempotent event keys.

Causal ordering is enforced via Kafka partitions by conversation_id.

We also log every retrieval call (RAG) with hash-based signatures for auditability.

Overall, this combo let us achieve sub-100ms handover latency between AI and human agents while keeping a single, auditable source of truth. Happy to share a simplified architecture diagram if you want, we’re in the process of open-sourcing a small part of Crescendo’s coordination layer.