Practical, evidence-grounded articles on building resilient automation workflows, designing stateful AI agents, integrating LLM APIs, and architecting robust backend systems.
Not every repetitive task deserves an automated pipeline. Here is an engineer's framework for assessing ROI, failure tolerance, and data readiness before writing a single line of automation.
Beyond the marketing buzzwords: a technical look at how state persistence, tool execution, and cyclical evaluation loops separate true agentic systems from standard conversational LLMs.
A deep dive into event-driven architecture, webhook security, handling retries, and designing idempotent consumers that prevent duplicate data operations.
Practical engineering techniques to prevent silent failures, handle LLM hallucinations, and build self-healing automation pipelines with n8n and Python.
Evaluating latency budgets, token cost economics, fallback strategies, data privacy boundaries, and vendor lock-in when adding AI to backend systems.