Custom AI Agents: Build, Deploy, and Run Your Own
Blog post from CodeWords
Custom AI agents bridge the gap between generic chatbots and specific workflow needs by incorporating four essential layers: reasoning, tools, memory, and execution. These agents require a reasoning engine, such as a large language model (LLM), to process context and make decisions; a tool layer for invoking integrations; a memory layer for maintaining state across interactions; and an execution layer to ensure reliable runtime. Unlike demo agents, production-ready agents emphasize error handling, state management, and deployment infrastructure. CodeWords facilitates the creation of custom AI agents with tools like Cody, offering access to a variety of LLMs, over 500 tool integrations, a Redis-based memory layer, and managed serverless execution environments, enabling users to define, build, test, and deploy AI agents effectively.
| Trend | Post Mentions | Total Month Mentions | Posts | Companies | MoM |
|---|---|---|---|---|---|
| AI Agents | 8 | 5,657 | 1,451 | 270 | -3% |
| LLM | 5 | 9,814 | 1,776 | 243 | +42% |
| Serverless | 2 | 1,846 | 630 | 102 | +131% |
Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.