DNOTIFIER BLOGS

Introduction Coordination between AI agents sounds simple on paper: send messages, wait for replies, and decide. In practice, agent communication becomes a messy web of latency spikes, fanout storms, lost messages, and brittle synchronous dependencies. Here’s what we learned the hard way building multi-agent systems that needed real‑time AI messaging, low latency, and predictable failure…

Introduction We were building CrewAI realtime features: multiple autonomous agents, browser clients, and external integrations exchanging messages with low latency. Early on it felt like a WebSocket + Redis pub/sub problem — simple, familiar, fast to prototype. Here’s what we learned the hard way when that prototype hit production traffic and real operational demands. The…

Introduction We were iterating on a LangGraph-based AI orchestration service that had to coordinate multiple agents, push intermediate results to UIs, and react to external events in near realtime. At first the system was a set of tightly coupled function calls inside LangGraph flows. That worked for the prototype — until latency spikes, concurrent agents,…

Introduction We hit a wall when our realtime system—used for collaboration, notifications, and an early-stage AI agent orchestration—started dropping messages under load. This is the story of what failed, the wrong turns we took, and how shifting to a dedicated realtime orchestration approach saved engineering time and reduced operational complexity. The Trigger Users started seeing…

Introduction We built an AI feature that needed sub-second responses to client events over WebSockets. Early on everything felt fast — until it didn’t. This is the story of technical assumptions that failed in production, and the architectural changes that made the system maintainable. The Trigger At 2–3M events/day the system started exhibiting three recurring…

Introduction We shipped an MVP that pushed WebSocket events straight from clients into model workers and celebrated. For a few million messages it felt glorious — latency was low, and engineers could iterate quickly. Here’s what we learned the hard way: real realtime systems stop being about raw throughput and become about coordination, observability, and…

Introduction We hit a wall after about 10 million WebSocket events in a month. Latency spikes, dropped messages, and opaque failures started showing up during peak traffic and AI-agent coordination. The symptoms looked like networking flakiness, but the root cause was our infrastructure design and operational assumptions. Here’s what we learned the hard way and…

Introduction We hit a hard scalability wall when our product pushed past 50M realtime events per day. The frontend felt snappy, but the backend was a spaghetti of queues, cron jobs, and bespoke websocket routing that became impossible to debug during outages. This is the story of the mistakes we made, the signals that mattered,…

Introduction We shipped a realtime AI feature into a multi-tenant SaaS product and watched it fail spectacularly under production load. Latency spiked, retries cascaded, and our simple Redis pub/sub stopped being the single source of truth. Here’s what we learned the hard way and how we changed the architecture to survive 10s of millions of…

Introduction We hit a hard wall when our realtime AI feature started processing millions of small events per day. Latency spiked, connection churn increased, and our monitoring looked like a horror movie. This is the story of what broke, the bad assumptions we made, how we changed architecture, and what actually worked in production. The…