Make AI agents: a practical build guide for 2026
Learn how to make AI agents from scratch — architecture decisions, tool selection, production patterns, and deployment strategies for real-world agents.
Osman Ramadan1 min read
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.