{
  "id": 14224035,
  "title": "Cron Scheduled Ollama Autonomous Agent",
  "url": "https://urgent.news/2026/10/13/cron-scheduled-ollama-autonomous-agent",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-13T15:00:29.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/apexgridtech/cron-scheduled-ollama-autonomous-agent-3338"
  },
  "original_language": "en",
  "account": "This week, the AI infrastructure space witnessed some impressive performance gains from modular systems compared to their alternatives. Meanwhile, our team has been quietly working on a unique use case that combines autonomous agents and on-device AI. The project involves a cron-scheduled Ollama-based social agent that interacts with users across various channels on our infrastructure.\n\nThe core of this system is Ollama, which provides a lightweight footprint and the capability to execute large language models locally. To achieve this, we've designed a modular architecture that separates different aspects of the process. Voice profiles are stored as individual files, while controversy is assessed using a second model, and credit checks are performed by querying a Postgres database. Any rejected content is stored in a quarantine area for later review.\n\nThe publish pipeline is managed by a function called publish_to_all_channels, which is triggered by a cron job every 12 hours. This function is responsible for generating content using Ollama, running it through multiple gates for checks, and publishing it only if all the checks are passed. The process starts with generating content, followed by a voice profile check. If the content fails this check, it is logged to quarantine and the process stops.\n\nNext, the content passes through a controversy gate using a separate model. If the content is deemed controversial or high-risk, it is also logged to quarantine and the process halts. The final gate involves a credit check, where the content is queried in the Postgres database. If the content is not found in the database, it is sent to quarantine, and the process ends. If all checks are passed, the content is published across all channels.\n\nThis modular approach allows for independent updating or swapping of each gate function. For instance, we could replace the controversy-checker model with a different one if necessary, or even adopt a rule-based system for specific scenarios. The quarantine system is equally crucial as it allows for reviewing content that fails any gate and potentially reprocessing it later if circumstances change.\n\nHowever, there are certain trade-offs with this approach. The use of cron jobs introduces a fixed interval between runs, which might not be suitable for real-time applications. Additionally, the current setup allows for only one content generation per run, which might not be efficient for high-frequency publishing. We are actively exploring ways to decouple scheduling from processing and are interested in event-driven architectures that could enable more dynamic content generation and publishing. We are also focusing on enhancing the performance of the controversy gate, experimenting with techniques like model quantization and caching to reduce latency. The question remains, what is the optimal balance between accuracy and speed in this kind of gate? We welcome your feedback on this topic.",
  "summary": "This week, the AI infrastructure space saw some eye-opening benchmarks, with modular systems showing dramatic performance gains over alternatives. Amid all the noise, we’ve been quietly iterating on a specific use case that sits at the intersection of autonomous agents and on-device AI: a cron-scheduled Ollama-based social agent that runs on our infrastructure and interacts with users across…",
  "key_points": [
    "Cron-scheduled Ollama-based social agent interacts across channels",
    "Modular architecture separates voice profiles, controversy assessment, and credit checks",
    "Quarantine system reviews rejected content for reprocessing"
  ],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}