Years ago, my university professor, Dr. Herwig Mannaert, shared an idea that stayed with me:
“In distributed systems, async works. Sync doesn’t. Or at least, it’s very, very hard to get working.”
At the time, he was describing how synchronous architectures in large-scale distributed systems often fail. When every component waits for the others to respond in real time, the design becomes fragile. Timeouts and cascading failures are common.
Asynchronous systems solve this. They decouple senders and receivers, so each part can operate at its own pace. They stay resilient under pressure. They work.
That same principle applies to AI agents today.
Synchronous vs asynchronous AI agents
Most AI systems today work in a synchronous way. You open a live chat, type a question, and wait for a response. You stay in the conversation, and if you step away too long, the session ends. The agent forgets.
It’s fast and interactive, but it demands your attention.
Now picture the same interaction running asynchronously. More like email, Slack, or WhatsApp. You send a request to an AI agent. It responds when it’s ready. Hours later, you can reply without losing context. The agent keeps the thread alive. It stays persistent and focused.
Both modes have value. Sync is great for quick resolutions. Async works best for complex workflows or when you need to keep context across hours or days.
Designing for both
If you’re building an AI-first platform to serve customers, developers, or internal teams, you can’t pick only one mode. You need both.
At DevRev, we’ve built our AI infrastructure to support multiple ways of working. It includes a few key capabilities:
- A unified knowledge graph that keeps real-time context across all channels
- Flexible support for chat, email, and tickets
- Persistent conversations that keep full history, no matter how long the pause
- Custom tone and access levels based on role and channel
This flexibility improves service quality and lowers costs. One well-designed AI agent, available all day and night, can replace a patchwork of tools and handoffs.
Agents that fit the way we work
Distributed systems proved that async works. AI systems should follow the same principle.
Work doesn’t always happen in real time. Conversations flow across channels and timelines. AI agents need to match that reality.
Don’t choose between synchronous or asynchronous. Build for both.
At DevRev, we already do.
