Posts tagged Agentic Systems — AI × software engineering, made practical.
Learn what CQRS actually splits between write and read models, why projection lag becomes a permanent fact you have to own once you adopt it, and how to expose staleness as data for both dashboards and AI agents.
You'll learn a quick test for telling whether a ReAct agent's Thought steps are genuine reasoning or just narration over an action space that only ever had one legal move.
A bloom filter can degrade from 1% wrong to 80% wrong without throwing a single error, and I only found ours because three near-identical tickets landed in the same hour at 2am.
A rate limiter that never rejected a single request took down four services at once — because 'queue everything' and 'protect the API' turned out to be two very different jobs.
A clean git revert made our dashboard look fine — it was the billing charges, duplicate emails, and spent API credits sitting in other people's databases that told the real story.
A vector DB looked like an obvious upgrade until the numbers came back, and by then the agent had been quietly answering from the wrong documents for weeks.
One team took 40 minutes to fix a 2am incident, the team next door took 10 seconds for the same severity — and the gap wasn't skill, it was a boolean someone remembered to build in advance.
It's not your prompt, your retriever, or your reranker — it's the embeddings you set once and never looked at again, and here's the diagnostic that catches it before it becomes an incident.
A cached number ninety seconds old looks byte-for-byte identical to a fresh one — until an agent stakes an irreversible decision on it, and nothing in your stack was ever built to notice the difference.
Most of the 'experiments' your team ships never had a shot at losing — here's the three-second question that tells you which kind you just ran.
A dead letter queue nobody reads isn't a safety net, it's a data-loss incident running on a delay — here's the replay path most teams never build, and why agentic pipelines make triage harder, not easier.
Two rows, one identical idempotency key, both attempts legitimate — the 3am postmortem that made us realize our retry wrapper wasn't resending a request, it was re-asking the model what it wanted to do.
An agent pipeline can post a spotless uptime dashboard for six straight hours while quietly solving the wrong problem entirely — here's the sensor a classic circuit breaker doesn't have, and the one you actually need.
A queue promises your message will arrive eventually — it never promised you could prove what happened to it, and that gap is what pages you when a delivery goes quietly, successfully wrong.
One 429 turned into a fan-out storm in under ten minutes — here's the assumption baked into every rate limiter that agentic callers blow straight through, and the three fixes that actually hold.
A gut-check for engineers who reach for Kafka by default: if the consumer can't perceive the latency win, streaming isn't buying speed, it's buying you an incident channel.
Your dashboards say every node is healthy, but one of them is quietly swallowing every expensive tool-use chain — because round-robin and least-connections were never built to know what an LLM request actually costs.