How to make AI agents that actually work in production

An AI agent is a program that observes, decides, and acts in a loop. Unlike generic AI automation posts, this guide shows real CodeWords workflows — not just theory. For broader tool comparisons, see AI workflow automation tools or workflow builder.

TL;DR

• Making AI agents requires three architectural decisions: reasoning loop pattern, tool access strategy, and termination logic.
• Most agent failures happen in tool integration and loop control — not in the LLM itself.
• CodeWords lets you build agents conversationally with Cody, provides native LLM access and 500+ tool integrations, and deploys agents as serverless microservices with built-in state persistence.

In CodeWords, this maps directly to the architecture: the LLM reasons (OpenAI, Anthropic, or Gemini), actions execute via integrations (500+ via Composio), and state persists in Redis between executions.

In CodeWords, tell Cody what you want. See CodeWords templates for pre-built agent patterns. CodeWords is that environment — conversational to start, code-level when you need it, production-ready from the first deployment.