4 Layers of Agent Systems: Why Your AI Agent Says It's Done But Actually FailedYour agent burned through tokens, declared success, and then failed in production. Is it a model problem? Is it a prompting problem? No. It's an architecture problem. The Real Issue When an LLM agent10 min read·sailokeshdevathi.hashnode.devAug 15
AI Governance in Production: How to Stop Agents from Overspending, Leaking Data, and Calling Tools They Shouldn'tYou have 10 different AI models, 15 custom agents, 8 external tools, and 0 visibility. Here's how to fix it. The Problem: Distributed AI Chaos Your company is deploying AI everywhere: Models: OpenAI12 min read·sailokeshdevathi.hashnode.devJul 31
Why Your LLM Times Out in Production and How to Fix It LLM worked perfectly in testing. Shipped it to production. Now users are complaining it takes 8 seconds to respond. Some requests are timing out. Monitoring shows high latency but no idea where to sta7 min read·sailokeshdevathi.hashnode.devJul 21
How to Evaluate LLM Agents in ProductionEvaluating a model is simple. Give it an input, check the output, measure accuracy. Evaluating an agent is completely different. An agent does not produce one output. It makes a plan, calls tools, rea13 min read·sailokeshdevathi.hashnode.devJul 12
MCP (Model Context Protocol) — How AI Agents Finally Got a Standard Way to Use ToolsEvery time your agent needs a new tool, someone has to write custom code to connect them. GitHub integration — custom code. Slack integration — custom code. PostgreSQL integration — custom code. File9 min read·sailokeshdevathi.hashnode.devJul 5
