From Log Chaos to Intelligence: How LogWatch Agent Revolutionises Operational Analysis

[pugupta]

 

This blog is written by Puneet Gupta

Have you ever been in this situation?

It's 3 AM. Production is down. Customers are hitting errors. Your phone won't stop ringing.

You grep through thousands of log lines across services, packet captures, and metrics dashboards. The clock is ticking. Every minute costs real money.

Finally, you find something:

ERROR: Database connection timeout

You've found an error. But is it the root cause or just a symptom? Why is the database timing out? Did something change? Is it the database, the network, or the application?

The answer is scattered across a dozen systems, and you don't have hours to piece it together manually.

Welcome to LogWatch

LogWatch doesn't just find errors — it tells you the complete story.

Application logs: 5,247 connection timeouts to PostgreSQL over 60 minutes

Network PCAP analysis: The same service shows 523,000 connection attempts — 100x more than any other client

GitLab pipeline logs: A deployment 90 minutes ago introduced a retry loop with aggressive polling

Root cause: Not a database problem. A configuration regression caused exponential retry behavior, creating a self-inflicted DDoS.

The root cause wasn't hidden. It was just buried.

Traditional approach:

4–6 hours → manual log analysis → blaming the database team → checking deployments → tribal knowledge → eventually finding the config issue

LogWatch approach:

3 minutes → automated cross-system correlation → precise root cause → actionable fix

This is intelligence-driven log analysis — the opposite of throwing raw logs at an LLM.

At Palo Alto Networks, this wasn’t an edge case. It was daily life in Network Security (NetSec).

Our CI/CD pipelines generated massive volumes of logs across Jenkins and GitLab. A single failure meant engineers and SREs manually grepping through noise, trying to infer where the failure started — and why. Real answers required correlating data across:

• CI/CD pipeline logs
• Garuda service logs
• Prisma Access telemetry
• Firewall and network logs
• Customer-specific signals across multiple tenants

All the data existed but none of it spoke the same language. The real problem wasn’t lack of observability — it was lack of intelligence.

Logs were telling us what happened, repeatedly. They weren’t telling us why.

The result was a system trapped in inefficiency:

• Maximum 87% pipeline pass rate — we never achieved 100%, even with clean builds
• Low robustness — cascading failures masked the true root cause
• Severe manual bottleneck — QA and SREs spent 60–80% of their time reading logs instead of testing or fixing issues
• Poor signal quality — errors looked like “something went wrong” instead of actionable root causes

The irony was painful.

We weren’t missing data — we were overloaded with it.

What we needed wasn’t more logs, dashboards, or alerts. We needed a solution that could preprocess and reason over logs before humans ever looked at them.

That insight led us toward an agent-based log intelligence approach:

• Preprocess and normalise logs across pipelines, services, and security platforms
• Automatically correlate failures across Jenkins, GitLab, Garuda, Prisma Access, and firewall telemetry
• Extract intent, causality, and failure patterns from raw logs
• Feed clean, structured context to Troubleshooting Agents, TSE Agents, and NetSec support workflows

Instead of engineers asking, “Where do I even start?”
The system should answer, “Here is the failure, here is the dependency chain, and here is the most likely root cause.”

That shift   from raw logs to agent-ready intelligence  is what transformed log data from a liability into an asset.

The False Promise: “Just Throw It at an LLM”

When large language models exploded in popularity, the knee-jerk reaction was obvious: dump the logs into GPT and get instant insights. Teams quickly discovered the harsh economics:

Why Raw LLM Analysis Fails

• Token costs explode: Production log files easily consume $50–200 per analysis
• Context limits hit immediately: Most LLMs cap at 128K tokens — your real logs are often 10x larger
• Signal drowns in noise: LLMs analyse health check spam with the same priority as critical errors
• No pattern learning: Each analysis starts from scratch with zero accumulated intelligence

The fundamental flaw: treating all log data as equally important.

LogWatch’s Breakthrough: Intelligent Preprocessing Architecture

LogWatch solves the log explosion problem with a hybrid architecture that combines deterministic preprocessing with targeted LLM analysis. Instead of blindly feeding raw logs into expensive language models, LogWatch builds an intelligent preprocessing pipeline that converts noisy, unstructured logs into high-signal, structured intelligence.

The result is faster analysis, dramatically lower cost, and insights that are directly usable by troubleshooting agents, TSE workflows, and NetSec operations.

TSE Agent

Jenkins pipeline correlation using logwatch

Regression Analysis: Logwatch

 

The Core Architecture

Raw Logs → Smart Preprocessing → Structured Intelligence → LLM Analysis → Actionable Results

Traditional approach
50,000 log lines → LLM API High cost → generic summaries of mostly irrelevant data

LogWatch approach
50,000 log lines → ~50 meaningful patterns → ~ Low LLM cost → precise, actionable insights tied to real failures

LogWatch ensures that LLMs reason only over signal, never over raw noise.

 

The Hybrid Regex + Zero-Shot Classification Engine

LogWatch’s core innovation lies in its multi-stage preprocessing pipeline, which combines rule-based efficiency with AI-driven flexibility.

 

1. Advanced Regex Preprocessing

Regular expressions (regex) are used deliberately for cost control, speed, and early signal extraction.

 

Smart Line Splitting Configuration

• Timestamp boundary detection
  Automatically splits logs using ISO and custom timestamp formats for accurate event reconstruction.
• Severity-aware prioritisation
  Error- and warning-level logs are prioritised over informational noise.
• Custom pattern support
  Configurable regex profiles for:
• CI/CD pipeline logs (Jenkins, GitLab)
• Observability logs
• Firewall and security logs
• Network and PCAP-derived logs

 

Label & Feature Extraction

• Identity signals
  User IDs, session IDs, request IDs
• Performance metrics
  Latency, response times, retries, timeouts
• Error semantics
  Error codes, failure types, and severity levels
• Domain-specific fields
  Extracted via pluggable, user-defined regex rules

 

Performance Benefits

• Massive reduction in token consumption before LLM analysis
• Faster processing through parallel regex execution
• Early noise elimination for repetitive or low-value logs
• Clean, structured inputs for downstream correlation and agents

 

2. Zero-Shot Classification Integration

Regex preprocessing is augmented with zero-shot AI classification, allowing LogWatch to enrich log patterns without any training data:

• Automatic categorisation of log patterns
• Severity inference based on semantic content
• Component identification (pipeline, network, auth, service, security)
• Impact assessment (customer-facing vs internal-only)

This makes LogWatch adaptable across products, customers, and environments with zero retraining.

 

3. Drain3 Clustering Algorithm

To avoid line-by-line analysis, LogWatch uses the Drain3 clustering algorithm to generate intelligent log templates.

 

Pattern Template Generation

Instead of analysing 50,000 individual log lines, LogWatch produces compact, high-signal patterns:

• Template
  "Database connection failed after <timeout> seconds"
• Frequency
  5,000 occurrences (15.5% of total logs)
• Variable extraction
  Timeout values, error codes, and user identifiers
• Sample preservation
  Representative examples retained for context

 

Intelligence Benefits

• Automatic grouping of similar failures
• Frequency-based prioritisation of dominant issues
• Variable extraction for metrics and diagnostics
• Elimination of repetitive log spam

 

The Correlation Engine: Cross-System Intelligence

Modern incidents rarely live in a single system. LogWatch’s correlation engine is designed to reason across pipelines, observability platforms, security systems, network data, and internal tools.

 

Intra-Connector Correlation

Within a single connector, LogWatch identifies:

• Temporal patterns in event sequences
• Cascading failures within the same system
• Performance degradation trends over time
• Error pattern evolution and frequency changes
• Inter-Connector Correlation

Across connectors, LogWatch correlates:

• CI/CD pipeline events ↔ runtime failures
• Observability signals ↔ deployment or config changes
• Network / PCAP anomalies ↔ application errors
• Security and firewall events ↔ customer impact

This enables accurate root-cause propagation across system boundaries.

 

LogWatch SDK: What Users Actually Build

LogWatch provides a connector SDK that allows users to contribute new integrations in a controlled and consistent way. To add a new connector, a user implements a single class that follows a well-defined interface. The goal is to keep user effort minimal while ensuring every connector can fully participate in preprocessing, correlation, and workflows.

 

Connector Responsibilities

A user-written connector is responsible for:

• Data acquisition
  Fetching data from an external source such as an API, file system, database, PCAP file, or internal portal.
• Parsing and normalisation
  Converting raw input into LogWatch’s standard event model so it can be processed and correlated consistently.
• Optional enrichment
  Applying lightweight logic such as regex extraction, Drain3 pattern hints, or domain-specific heuristics when needed.
• Optional RAG integration
  Attaching external knowledge sources (runbooks, historical incidents, documentation) to enrich the events with additional context.
• Agent interface
  Exposing structured findings that can be consumed by other agents during collaborative analysis.

LogWatch SDK

 

What the Platform Team Provides

The LogWatch platform abstracts away all common infrastructure concerns by providing:

• Base connector classes and interfaces
• Standard schemas, validation, and error handling
• Built-in preprocessing primitives, including regex extraction, Drain3 clustering, and zero-shot classifiers

This separation allows users to focus only on what makes their data source unique, while LogWatch handles scale, consistency, and intelligence.

 

Built for Agents, Not Just Humans

LogWatch is designed as an intelligence layer for agents:

• Troubleshooting agents consume structured patterns
• TSE agents get customer-specific correlated insights
• NetSec agents reason across the pipeline, network, and security data

Instead of asking engineers to read logs, LogWatch prepares the data so agents can reason, correlate, and act.

This is a strong set of results. To make them even more impactful for a report or presentation, I’ve reorganised the information to highlight the Efficiency Gains and Strategic Impact.

 

Result: Logwatch Performance & Engineering Impact

Logwatch has successfully moved from integration to a core driver of engineering efficiency. By automating high-toil tasks, the system has shifted the focus from reactive firefighting to proactive development.

 

1. Operational Efficiency & Metrics

The following table summarises the quantitative improvements across key engineering pillars:

Before LogWatch, finding the root cause of a build failure took 4–6 hours. Engineers manually checked pipeline logs, observability data, and Jira tickets. Triage was 100% manual, slow, and error-prone.

With LogWatch, root cause analysis now takes minutes, reducing downtime by 95%+. Jira triage effort dropped to ~40% manual, saving 60% of engineering time. What used to be manual or script-driven is now fully automated, eliminating human error.

 

2. Workflow Integration & Scope

Logwatch is currently embedded across three critical high-impact areas:

• Automated Failure Analysis: Real-time diagnostics for Build and Test Case failures.
• Intelligent Triaging: Summarisation and prioritisation of incoming Jira tickets.
• Operational Agility: Automated playbook execution and Cloud Productivity Tools (CPT) integration.

 

3. The “Ripple Effect” on Velocity

The impact of Logwatch extends beyond individual ticket resolution:

Mainline Health: By reducing build failure diagnostics from half a day to minutes, Logwatch ensures the main branch remains “green.”

Commit Velocity: A healthy main branch eliminates developer “wait states,” directly increasing the frequency and reliability of code commits across all engineering pods.

Team 

Puneet gupta https://www.linkedin.com/in/puneetggupta/
Peter Kirubakaran N  https://www.linkedin.com/in/peter-kirubakaran-n-a3225621/ 

Sughosh Divanji  https://www.linkedin.com/in/sughosh-divanji-b0a1021b/ 

Kuldeep Saini  https://www.linkedin.com/in/kuldeep-s-8ab5021a4/