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 agentAug 15 · 10 min readsailokeshdevathi.hashnode.dev
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: OpenAIJul 31 · 12 min readsailokeshdevathi.hashnode.dev
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 staJul 21 · 7 min readsailokeshdevathi.hashnode.dev
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, reaJul 12 · 13 min readsailokeshdevathi.hashnode.dev
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. File Jul 5 · 9 min readsailokeshdevathi.hashnode.dev
