Why AI Agents Disappear in Production (and How to Keep Them Running for $5.70/Month)

Minimal Agent Loop (Python)

To create a minimal agent loop using llama-cpp-python or local LLMs, you can follow the example below:

python
 
1import os
2from langchain.llms import LlamaCpp
3from langchain.agents import initialize_agent
4
5# Initialize the LLM with your model path and context size
6llm = LlamaCpp(model_path="./models/phi-2.Q4_K_M.gguf", n_ctx=2048)
7
8# Define the tools that the agent will use
9tools = [your_tool_here]
10
11# Initialize the agent with the specified tools, language model, and agent type
12agent = initialize_agent(tools, llm, agent="zero-shot-react-description", verbose=True)
13
14# Run a task using the agent and print the response
15response = agent.run("Your task here")
16print(response)

Step 3: Cost Control with Health Checks

To ensure your AI agent remains up and running, you can set up periodic health checks. A simple Bash script to ping your agent endpoint every 5 minutes is provided below:

bash
 
1#!/bin/bash
2curl -sSf https://your-agent-endpoint.com/health || docker restart
3
4[Read the full article at DEV Community](https://dev.to/tamizuddin/why-ai-agents-disappear-in-production-and-how-to-keep-them-running-for-570month-399e)
5
6---
7
8**Want to create content about this topic?** [Use Nemati AI tools](https://nemati.ai) to generate articles, social posts, and more.