WARDEN: Hunting the Ghost in the Machine with an AI-Powered Self-Healing DevOps Agent

A developer built WARDEN, an open-source AI-powered self-healing DevOps agent that follows a Detect → Diagnose → Remediate → Verify workflow against a FastAPI payment-service simulator. The agent uses open-weight models via Nebius Token Factory's OpenAI-compatible API, restricts remediation to a fixed set of permitted actions with operator approval, and confirms recovery by measuring actual HTTP responses rather than trusting the agent's claims. The project ships with 18 automated tests and a deterministic Demo Mode that runs without API credentials.

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 4: Ghost in the Machine https://dev.to/challenges/hacktoberfest-week4-2026-10-26 WARDEN is an AI-powered self-healing DevOps agent that detects service failures, investigates their root causes, proposes safe remediation actions, and verifies whether the system has actually recovered. Every production system has its ghosts. A sudden traffic spike. An exhausted connection pool. Unexpected HTTP 503 errors. A service that appears healthy but silently struggles under concurrent load. These failures can be difficult to diagnose. Engineers often spend valuable time switching between logs, metrics, configuration files, and monitoring dashboards. I wanted to explore a different approach: What if an AI agent could investigate incidents and help resolve them while keeping safety and human approval at the center? That idea became WARDEN. WARDEN follows a four-stage incident response workflow: Detect → Diagnose → Remediate → Verify For the demonstration, WARDEN manages a FastAPI-based payment-service simulator. I can deliberately introduce connection-pool exhaustion, causing the service to return HTTP 503 errors under concurrent traffic. WARDEN then: The important part is that WARDEN does not simply announce that an incident is fixed because a configuration changed. It verifies recovery using actual HTTP responses. Watch WARDEN in action: ▶️ Watch the WARDEN Project Demo on YouTube https://youtu.be/rc6QmCs6KbI The demonstration showcases the project's incident-response workflow and recovery capabilities. The project is open source and available on GitHub: WARDEN - AI-Powered Self-Healing DevOps Agent https://github.com/uxzwal/Warden-AI-Powered-Self-Healing-DevOps-Agent WARDEN/ │ ├── warden/ │ ├── app.py │ ├── payment service.py │ ├── agent.py │ ├── nebius client.py │ ├── tavily client.py │ ├── verifier.py │ ├── remediation.py │ ├── schemas.py │ └── audit.py │ ├── tests/ ├── requirements.txt ├── pytest.ini └── README.md The repository includes automated tests for service behavior, remediation safety, agent output validation, and empirical recovery verification. WARDEN combines an AI reasoning layer with deterministic execution controls. The agent architecture supports open-weight models through Nebius Token Factory's OpenAI-compatible API. The documented model configurations include: The architecture separates fast triage from deeper reasoning so that model selection can match the complexity of an incident. A deterministic Demo Mode is also available, allowing the incident-response workflow to operate without API credentials. Giving an AI agent unrestricted access to production infrastructure would introduce serious operational risks. WARDEN takes a constrained approach. The agent cannot execute arbitrary shell commands, Python code, SQL, or unrestricted operations. Instead, remediation is limited to explicitly permitted actions: increase pool limit reset service config no op Numeric boundaries are enforced through deterministic validation, and configuration changes require explicit operator approval. WARDEN uses HTTPX and asynchronous requests to evaluate service behavior under concurrent load. It measures actual HTTP responses and performance indicators instead of trusting the agent's claims. A proposed fix is not the same thing as a verified recovery. The project includes 18 automated tests covering: Open innovation matters because infrastructure automation should not depend on a single closed model or an opaque decision-making system. Using open-weight model options through an inference platform such as Nebius Token Factory provides flexibility in model selection while allowing developers to retain control over the surrounding application architecture. For WARDEN, the reasoning model is only one component of the system. The application also contains independently testable safety validators, structured schemas, deterministic remediation rules, and recovery verification. This separation makes it possible to experiment with different models without giving them unrestricted control over infrastructure operations. Open innovation also makes it easier for developers to inspect, adapt, test, and extend the system according to their own operational requirements. WARDEN's incident lifecycle can be demonstrated through its dashboard: Incident → Diagnosis → Approval → Remediation → Verification The most interesting part of building WARDEN was realizing that an AI agent should not be judged only by how intelligently it explains a problem. It should also be judged by what happens after its recommendation. Does the service actually recover? Can the action be trusted? Can an engineer understand what changed? And can the system prove that the incident is resolved? I wanted to build something that goes beyond an AI chatbot suggesting commands. WARDEN represents an approach where AI reasoning works together with deterministic safety, human oversight, and measurable outcomes. The ghost in the machine might be invisible, but its impact is measurable. WARDEN: Find the failure. Understand the cause. Fix it safely. Prove the recovery.