TraceForge — Self-Validating AI Reliability Engineer

TraceForge is an autonomous AI reliability engineering platform that transforms how developers diagnose, investigate, and resolve failures in complex distributed systems. Traditional AI coding assistants can suggest fixes, but they cannot reliably determine whether those fixes actually work. TraceForge addresses this limitation by introducing self-validating AI agents that combine reasoning with real-world experimentation. Powered by Meta Model API, TraceForge analyzes distributed traces, application logs, performance metrics, and source code to investigate failures across interconnected microservices. Its core innovation is an autonomous Hypothesize–Experiment–Verify loop: 1. Understand: Analyze system telemetry and reconstruct service dependencies to identify potential failure causes. 2. Reason: Generate and prioritize root-cause hypotheses using Meta's AI models. 3. Experiment: Execute targeted diagnostic actions and reproduce failures inside isolated environments. 4. Verify: Test proposed fixes against measurable system behavior, automatically rejecting solutions that fail validation. 5. Explain: Produce evidence-backed recovery recommendations with transparent reasoning and human approval for production changes. The architecture combines Meta Model API, agentic tool calling, retrieval-augmented generation (RAG), OpenTelemetry, Python, and containerized testing environments. For the hackathon, we will demonstrate TraceForge detecting an injected microservice failure, investigating its root cause, and experimentally validating a recovery strategy. My vision is to move beyond AI that merely generates answers toward AI that can formulate hypotheses, conduct experiments, and verify its own decisions. TraceForge represents a step toward autonomous, trustworthy AI systems capable of understanding and improving real-world software infrastructure.