We’ve all seen it. The demo runs perfectly. The AI agent answers instantly, retrieves relevant docs, and executes actions like magic. Everyone claps.
Then it hits production. And the magic leaks out through the cracks.
- The retrieval layer serves stale data because the index lagged.
- The “send-update” action fires twice because the workflow retried mid-execution.
- A minor outage in a tool API stalls the entire reasoning chain.
This isn’t an “AI problem.” It’s the same distributed systems problem we’ve been fighting for decades — only now the failure modes are harder to spot because the system is draped in AI hype.
🔗The AI Era’s Architectural Blind Spot
Most AI products today are built like this:
- Prompt → Model → Output → Side Effect
- Wrap with some glue code and a UI
- Ship
That’s fine — until you add:
- Retrieval-augmented generation (RAG) pulling from a vector DB.
- Multiple agents handing off tasks.
- Tool calls with unpredictable latencies.
- Feedback loops for model tuning.
Now you’ve got a multi-node, multi-hop distributed system. The rules change — or rather, they don’t change, but they suddenly matter.
🔗Why the Cracks Show Faster in AI Systems
Distributed systems always have cracks. AI makes them wider because:
- Data freshness matters more — stale embeddings don’t just delay an update, they mislead the model.
- Side effects are harder to reverse — you can’t “un-send” a wrong support ticket.
- Failures compound invisibly — a hallucination in step 2 poisons steps 3–5 before you notice.
When you ignore the plumbing, the first real user spike or partial outage is enough to turn your “production AI” into a distributed hallucination generator.
🔗Where This Series Fits In
Over the next few posts, I’m going to break down the patterns and practices that make AI systems predictable under load and recoverable under failure.
Not theory. Not “you should implement Raft.” Real, implementable patterns that work in:
- RAG pipelines.
- Multi-agent orchestration.
- Long-running workflows with side effects.
- AI toolchains calling flaky external services.
We’ll get into how to make the plumbing as intelligent as the model — so you don’t just ship AI, you keep it alive.
🔗One Small Fix That Pays Off Immediately
If you’ve got an AI feature that takes any action — send an email, create a Jira ticket, kick off a deployment — give it an idempotency key.
It’s not glamorous. But the first time your workflow retries and doesn’t double-charge a customer or flood a channel, you’ll be glad you did.
In the AI era, it’s not enough for the model to be smart. The system around it has to be smarter about failures.