Blog
The Tumult blog series covers the platform end-to-end, from first principles to advanced use cases.
| Post | Topic |
|---|---|
| Introducing Tumult | What Tumult is, why it was built, and how it differs from existing tools |
| The AI Advantage | How TOON’s token efficiency enables AI-native chaos analysis |
| Built-In Observability | OpenTelemetry spans and resilience.* attributes — always on, zero config |
| The Plugin System | Script plugins, native plugins, discovery order, and writing your own |
| The Experiment Format | Deep dive into TOON experiment structure, providers, and tolerances |
| The Analytics Pipeline | DuckDB + Arrow + Parquet: SQL over your chaos history |
| Kubernetes Chaos | tumult-kubernetes: pod delete, node drain, deployment scaling |
| Statistical Baselines | IQR, percentile, mean/stddev — replacing magic numbers with evidence |
| Compliance as Code | DORA, NIS2, PCI-DSS 4.0 — experiments as regulatory evidence |
| Chaos Under Load | Combining tumult-network and tumult-loadtest for realistic fault testing |
| The Road Ahead | MCP integration, Phase 3–5, and the future of autonomous resilience |
| The Full Span Waterfall | Real SigNoz traces from a live Tumult experiment — the observability proof |
Table of contents
- Introducing Tumult: Rust-Native Chaos Engineering for the Age of AI
- The AI Advantage: Why TOON Changes Everything
- Built-In Proof: Native Observability with OpenTelemetry
- The Plugin System: Chaos Engineering Without a Rust Toolchain
- Writing Your First Experiment: The TOON Format in Depth
- Data-Driven Chaos: SQL Analytics Over Experiment Journals
- Kubernetes Chaos: Deep Fault Injection with tumult-kubernetes
- Statistical Baselines: From Magic Numbers to Data-Derived Tolerances
- Compliance as Code: DORA, NIS2, and Regulatory Evidence with Tumult
- Chaos Under Load: Network Faults and Load Testing
- The Road Ahead: Autonomous Chaos, MCP, and the Future
- The Full Span Waterfall: Tumult Traces in SigNoz