LogPilot

Modern applications generate enormous amounts of logs, but when something starts failing, engineers often have to manually search through thousands of log lines, correlate errors across services, identify the root cause, and decide what to do next. Traditional monitoring can tell a team that something is wrong, but it often does not explain why it is happening or provide enough context to act before an incident becomes severe. LogPilot addresses this gap by turning raw application logs into a continuously updated picture of system health, risk, and likely failure. LogPilot is an autonomous log-intelligence agent that continuously ingests, parses, redacts, indexes, and analyzes logs from live services. It combines deterministic statistics and machine-learning algorithms with an agentic AI layer. Error velocity, baseline deviation, historical incident similarity, anomaly detection, DBSCAN clustering, and Isolation Forest are computed by the platform itself rather than guessed by an LLM. Root-cause analysis uses lagged Pearson correlation between service error time-series and trace-ID relationships to construct a causal chain. The AI then explains these already-computed results in natural language rather than inventing metrics or evidence. LogPilot also provides proactive failure forecasting. Services are continuously rescored on a roughly five-second cadence, allowing the platform to identify rising failure risk and generate pre-incident alerts and pre-mortems. Its conversational interface is genuinely agentic: instead of matching questions to fixed rules, the reasoning model receives the available tool catalogue and decides which tools to call, in what order, and how many times. For example, a question about a failing Redis service can cause the agent to independently combine root-cause analysis with targeted log searches before producing an answer.  To demonstrate the complete system in action, we built a separate demo e-commerce platform that generates realistic application activity and failures, with its logs continuously streamed into LogPilot in real time. We use Locust to generate sustained concurrent traffic against the platform, allowing LogPilot to continuously observe fresh live logs and demonstrate the complete detect, analyze, forecast and explain  workflow on real system activity rather than a scripted dataset.