Agent Chat for Logs: AI-Powered Log Analysis That Speaks Your Language
Transform complex log analysis into simple conversations. Upload CSV files and ask questions in plain English
Searching through gigabytes of log files using grep, awk, and regex patterns is tedious, error-prone, and time consuming. What if you could just ask your logs questions and get intelligent answers instantly?
Agent Chat for Logs transforms log analysis from command-line archaeology into natural conversations, making debugging accessible to everyone on your team.
The Log Analysis Challenge
Logs are one of the richest sources of debugging information, but they're also one of the hardest to analyze effectively:
Volume Overload
Applications generate millions of log lines per hour. Finding the needle in the haystack requires expertise and time.
Query Complexity
Mastering grep, awk, jq, and various log query languages takes months. Not everyone on the team has these skills.
Context Loss
Individual log lines lack context. Understanding what happened requires correlating events across time and services.
How Agent Chat for Logs Works
Step 1: Upload Your Logs
Export logs from any source (Elasticsearch, Splunk, CloudWatch, or raw files) as CSV. Agent Chat automatically understands the structure, fields, and relationships.
Step 2: Ask Questions in Plain English
No regex or query syntax needed. Just describe what you're looking for naturally.
"Show me all 500 errors from the payment service in the last hour"
"What happened right before the timeout errors started?"
"Find all database connection errors and group by service"
Step 3: Get Intelligent Insights
Agent Chat doesn't just filter logs it understands patterns, correlates events, and provides actionable insights.
AI Response:
Found 47 timeout errors in payment-service between 14:35 and 14:42.
Pattern: All timeouts occurred after database connection pool exhaustion (max_connections=100 reached at 14:34).
Recommendation: Increase connection pool size or investigate connection leak in recent deployment.
Powerful Use Cases
Incident Investigation
Quickly find the root cause during outages
"Show me all errors and warnings from 10 minutes before the alert fired"
Bug Reproduction
Trace user journeys through log events
"Find all requests for user ID 12345 that resulted in errors"
Pattern Analysis
Identify trends and anomalies
"What's the hourly distribution of 404 errors over the past week?"
Correlation Discovery
Connect related events automatically
"Show me logs from all services involved in this transaction"
Under the Hood
Intelligent Parsing
Automatically detects log format (structured, semi-structured, unstructured), extracts fields, timestamps, severity levels, and service identifiers.
Semantic Search
Understands the meaning behind your questions. "Database problems" automatically includes connection errors, timeouts, deadlocks, and slow queries.
Event Correlation
Links related log entries using trace IDs, request IDs, user sessions, and temporal proximity to build complete stories.
Fast Indexing
Columnar storage and smart indexing enable sub-second queries across millions of log lines, even on large CSV files.
Results Teams Are Seeing
Start Analyzing Logs Conversationally
No more grep wizardry required. Just upload and ask questions.
Shafi Khan
Founder & CEO, AutonomOps AI
Building the future of autonomous site reliability engineering. Former AI/ML leader at VMware, passionate about eliminating toil and letting engineers focus on what matters.