Your AI agent made 47,000 API calls yesterday. It logged 2.3 million events. Your observability bill is growing faster than your revenue.
Here's the uncomfortable truth: most of that data is noise.
The Over-Logging Trap
When teams first deploy AI agents, they log everything. Every API call. Every state change. Every decision point. It feels responsible—more data means more visibility, right?
Wrong.
More data means:
- Higher storage costs
- Slower queries
- Alert fatigue
- Harder debugging
- Compliance complexity
You end up with petabytes of logs that nobody reads and insights that nobody finds.
Signal vs Noise
Signal is information that:
- Changes your understanding
- Triggers action
- Proves compliance
- Enables improvement
Noise is everything else.
A Simple Framework
Before logging anything, ask:
1. Would I act on this?
If the answer is no, don't log it. A life counter decrementing from 20 to 19 in a game? Nobody's acting on that. The game ending with a player at 0 life? That's an outcome worth tracking.
2. Would an auditor need this?
Compliance requirements are specific. "Agent approved refund for customer X" matters. "Agent rendered button component" doesn't.
3. Would this help me debug a problem?
When something goes wrong, you need context. But you need relevant context. The decision that led to the error matters. The 50 successful operations before it probably don't.
4. Is this a decision or an action?
Decisions are high-signal. Actions are often noise. Log "agent chose to escalate" not "agent called escalation API."
What Changes
When you shift from logging everything to logging signal:
| Before | After |
|---|---|
| 2.3M events/day | 45K events/day |
| $12K/month storage | $400/month storage |
| 200ms query time | 15ms query time |
| 47 alert rules | 8 alert rules |
| 3% alert response rate | 89% alert response rate |
The numbers look different for every organization, but the pattern is consistent: less data, more insight.
The Empress Approach
Empress is built around signal, not noise. The platform encourages you to log:
- Decisions: What the agent chose and why
- Outcomes: What happened as a result
- Exceptions: When things went wrong
- Overrides: When humans intervened
Everything else is optional. You can log it if you want, but the platform doesn't push you toward noise.
Getting Started
This is the first post in a series on signal-focused observability. We'll cover:
- This post: Introduction to signal vs noise
- Deciding what to log: A practical framework
- The true cost of over-logging
- Decision-first observability
- Log levels for AI agents
- Compliance-driven logging
- Real-time vs batch observation
- The minimum viable audit trail
- Scaling your observability strategy
- Measuring observability ROI
The goal isn't to log less for the sake of logging less. It's to log better—capturing what matters and ignoring what doesn't.
Your observability system should make you smarter, not just store more data.