Drishti Shah

A single LLM call has one shape: request in, response out. You log it, alert on it, and move on. Agents break that model.  They make multiple LLM calls per task, decide which tools to invoke, and chain those decisions across steps to reach an outcome. By the time something

GitHub Copilot feels simple when a few developers use it. When entire teams depend on it every day, and platform engineers are expected to explain who is using it, how requests are routed, and where usage actually shows up across environments, things start to get complex. At this stage, Copilot

n8n works reliably in a single-developer status. The picture changes once AI Agent nodes begin calling LLMs across shared teams and environments. Token usage is hard to track, provider keys are spread across credentials, and workflows depend on a single model endpoint. The gap is not in workflow design. It

Your AI agent has been routing requests incorrectly, but your dashboards still show green because the infrastructure is healthy. Traditional ops were built for infrastructure systems. They do not account for workloads where correctness depends on prompts, models, and multi-step reasoning. This guide outlines what enterprises should expect from an