7-Day AI Agents: 5 Production Design Patterns for Long-Running LLM Workflows
3 months ago 高效码农Building AI Agents That Run for Seven Days: Five Production Design Patterns You spend weeks perfecting prompt engineering, fine‑tuning tool calls, and reducing response latency. Then your agent needs to stay alive for five days. Suddenly, those weeks don’t matter as much. Real production workflows — processing thousands of insurance claims, running week‑long sales sequences, reconciling financial data across systems — don’t fit inside a single conversation turn. They take days, not seconds. The moment you start building these long‑running agents, you hit a wall. Most agent architectures are stateless. They reconstruct context from a database on every interaction. And …
Agent Harness: The Secret Infrastructure Behind Production-Grade AI Agents
3 months ago 高效码农Agent Harness: The Core Infrastructure for Building Production-Grade AI Agents Have you ever faced this frustrating situation? You spend a lot of time building an AI chatbot that responds accurately and uses tools to complete simple tasks in demos. But when you try to turn it into a production-grade application, everything falls apart—the model forgets what it did a few steps ago, tool calls fail silently, and the context window gets filled with useless information, causing the entire agent’s performance to plummet. If you blame the large language model (LLM) itself, you’re probably looking in the wrong direction. The real …
Multi-Agent Collaboration Patterns: How to Choose the Right One for Your AI Project
3 months ago 高效码农Five Multi-Agent Collaboration Patterns: How to Choose and Use the Right One The core question this article answers: When you need multiple AI agents to collaborate on a task, which pattern should you use? How do you know which one fits your scenario? And what happens if you pick the wrong one? I’ve seen many teams pick a multi-agent pattern because it sounded “impressive” rather than because it actually fit their problem. That approach usually leads to trouble. Here’s my straightforward advice: start with the simplest pattern that can possibly work, watch where it hits bottlenecks, then evolve from there. …