Aggregate Logs and Set Alerts Early

LLM-powered automations often succeed in demos but fall apart in production. This series shares practical ways to make those systems reliable, scalable, and maintainable.

Today’s tip: centralize your logs and set up alerts before your system breaks.

Why This Matters

Logging to a local file or the console is useful, but it doesn’t scale.

When your automation runs across containers, cloud services, or machines, raw log files become a liability. You need a central system that can collect, filter, and alert on logs in real time.

Without log aggregation and alerts, your automation might fail silently, or worse, generate incorrect results while everything looks fine.

What to Do

Route your logs to a centralized system that supports:

• Structured log ingestion (ideally JSON)
• Filtering by field (e.g., log level, service, step)
• Real-time alerting
• Dashboards for observability

Here are some good options:

Tool	Strengths

Graylog

Open-source, supports GELF, great for structured JSON

Splunk

Enterprise-grade, highly scalable and powerful

ELK Stack (Elasticsearch, Logstash, Kibana)

Popular and flexible

Grafana + Loki + Fluent Bit

Lightweight and cloud-native

Production Tip

Don’t wait for your automation to fail in production; aggregate logs and set up alerts while things still work.

Code Example

Here’s how to forward structured logs from your LLM app to Graylog using Fluent Bit.

fluent-bit.conf

[SERVICE]
    Flush        5
    Daemon       Off
    Log_Level    info

[INPUT]
    Name         tail
    Path         /var/log/llm-automation.log
    Parser       json
    Tag          llm

[PARSER]
    Name         json
    Format       json
    Time_Key     timestamp
    Time_Format  %Y-%m-%dT%H:%M:%S

[OUTPUT]
    Name         gelf
    Match        llm
    Host         graylog
    Port         12201

Mount this config in your Docker Compose setup alongside your automation container. Fluent Bit will stream log entries to Graylog in real time.

Example Output

Here’s what one of your logs might look like in Graylog:

{
  "timestamp": "2025-08-03T14:36:42",
  "level": "ERROR",
  "prompt": "Generate a contract for a freelancer",
  "response": "I cannot comply with that request.",
  "source": "openwebui",
  "session_id": "abc123"
}

Going Further

Once your logs land in a system like Graylog, you can:

• Filter logs to isolate failed prompts, slow responses, or long token counts.
• Set alerts:
  • When error rates rise
  • If a specific LLM prompt starts failing
  • When response latency spikes
• Build dashboards:

Final Thought

Log aggregation doesn’t just store errors—it gives you the power to spot and respond to them. Alerts are how your automation asks for help when something goes wrong.

Log aggregation and alerting (plus the filtering and reporting that goes along with it) is a big step toward making your AI solution production ready.