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Why RANKIGI
Purpose-built AI governance vs general-purpose tools.
| Feature | RANKIGI | Datadog | W&B | DIY Logging |
|---|---|---|---|---|
| Tamper-evident chain | ✓ | ✗ | ✗ | ✗ |
| Agent identity (KYA) | ✓ | ✗ | ✗ | ✗ |
| Passive sidecar | ✓ | Partial | ✗ | ✗ |
| Legal evidence export | ✓ | ✗ | ✗ | ✗ |
| Behavioral drift | ✓ | Partial | ✓ | ✗ |
| Monthly gov report | ✓ | ✗ | ✗ | ✗ |
| Reflect mode | ✓ | ✗ | ✗ | ✗ |
| EU AI Act ready | ✓ | Partial | ✗ | ✗ |
| Built for agents | ✓ | ✗ | ✗ | ✗ |
| Immutable DB layer | ✓ | ✗ | ✗ | ✗ |
RANKIGI is purpose-built for autonomous AI agents. General observability tools like Datadog were designed for infrastructure and applications — not for governing agents that make decisions and take actions on behalf of humans. ML monitoring tools like Weights & Biases track model performance, not agent behavior. And DIY logging lacks the cryptographic tamper-evidence, immutability, and compliance mapping that enterprise governance requires.