Custom AI agents: how to build ones that actually run in production

Custom AI agents are the gap between what off-the-shelf chatbots do and what your specific workflow requires. The appeal is clear: an agent that understands your data, uses your tools, follows your rules. The challenge is that "custom" usually means "built from scratch," and most scratch-built agents die in a notebook.

The direct answer: a production custom AI agent needs four layers — a reasoning engine (LLM with good tool-calling support), a tool layer (integrations it can invoke), a memory layer (state across conversations or tasks), and an execution layer (where it runs reliably).

TL;DR

• Custom AI agents need four layers: reasoning (LLM), tools (integrations), memory (state), and execution (runtime) — skip any layer and the agent fails outside demos.
• The difference between a demo agent and a production agent is not intelligence. It is error handling, state management, and deployment infrastructure.
• CodeWords lets you build custom AI agents through Cody, with built-in LLM access, 500+ tool integrations, Redis state, and managed serverless execution.

What is the architecture of a custom AI agent?

The reasoning engine. This is the LLM — GPT-4o, Claude 3.5 Sonnet, Gemini Pro. Its job is to take context and decide what to do next. CodeWords provides access to OpenAI, Anthropic, and Google Gemini without API key management.

The tool layer. Tools are the functions your agent can invoke. CodeWords connects to 500+ tools through Composio and Pipedream, plus native integrations with Slack, WhatsApp, Airtable, and Google Drive.

The memory layer. Short-term memory is the LLM's context window. Long-term memory needs a persistence layer — Redis in CodeWords.

The execution layer. CodeWords provides ephemeral E2B sandboxes for isolated execution and deploys agents as serverless FastAPI services.

How do you build a custom AI agent step by step?

Step 1: Define the agent's job. Write a one-sentence description.

Step 2: Identify the tools. List every external action the agent needs.

Step 3: Design the agent loop. Map out the decision points.

Step 4: Build with Cody. Describe the agent to Cody.

Step 5: Test with real data.

Step 6: Deploy and monitor.