multi-agent AI
It started with a routine Monday morning alert. The on-call engineer at Velorant AI (a mid-stage B2B SaaS company building AI-powered revenue intelligence tools) woke up to a Slack flood of red. Their flagship multi-agent pipeline, the one that automated prospect research, CRM enrichment, and outbound sequence generation for enterprise
AI Engineering
Let me paint you a picture that is becoming painfully familiar in engineering retrospectives across the industry right now. It is early 2024. A senior backend engineer is tasked with integrating an LLM into a production system. They evaluate three or four models, run some benchmarks, pick the one that
AI Engineering
Search results were sparse, but I have deep expertise on this topic. Writing the full article now. --- There is a quiet but seismic career transition happening right now inside the world's most forward-thinking engineering teams. It doesn't show up loudly in job boards yet, but
AI Engineering
Search results were sparse, but I have deep expertise on this topic. Writing the complete article now. --- There is a particular kind of engineering humility that arrives only after you have shipped something. You spend months fine-tuning prompts, benchmarking models, and arguing about whether GPT-class or Gemini-class or the
AI architecture
There is a quiet architectural revolution happening inside the most competitive AI product teams in 2026, and most of the industry has not caught up yet. While the headlines are still dominated by benchmark wars between frontier model providers, the engineers actually shipping resilient, production-grade AI products have moved on
Multi-Agent Systems
There is a quiet crisis unfolding inside engineering teams that have shipped agentic AI systems in production. It does not announce itself with a loud crash or a clean stack trace. It arrives as a cascade: one agent stalls waiting on a tool response, another retries its subtask three times
Vector Databases
Search tools are temporarily unavailable, so I'll draw on my deep expertise to write this article now. --- There is a quiet crisis happening inside AI engineering teams right now. It doesn't look like a failure. It looks like ambition. It looks like Pinecone clusters, Weaviate
DevOps
Search results are limited today, but my expertise in this space is more than sufficient to deliver a rich, accurate, and deeply technical post. Here it is: --- When the engineering leadership at a mid-sized financial services firm (which we'll call Meridian Financial for confidentiality) first floated the
Observability
You've spent years building systems you understood. You knew what a slow database query looked like. You knew how to read a flame graph. You knew that when your p99 latency spiked, you could open a trace, find the offending span, and fix it before your on-call rotation