Exploration Phase

Chaos Engineering

Break things on purpose — so production doesn't break by accident.

"The best way to avoid failure in production is to practice failure in staging."

Netflix invented Chaos Monkey to randomly kill servers. Nika applies the same philosophy to AI workflows: inject failures, observe behavior, validate resilience — before your users discover the bugs.

Inspired by Netflix: Their Simian Army (Chaos Monkey, Latency Monkey, Chaos Gorilla) pioneered resilience testing at scale. We're bringing these principles to AI workflows where non-deterministic behavior makes reliability even more critical.

Why AI Workflows Need Chaos Engineering

Traditional chaos engineering targets infrastructure. AI workflows are inherently more chaotic:

Non-Deterministic Outputs

Same prompt, different results. Temperature, sampling, and model updates create variance.

Hidden State

Context windows, attention patterns, and internal model states are opaque.

Provider Dependencies

Rate limits, outages, and API changes from third parties. No control.

Cascading Failures

One bad output feeds the next agent. Errors amplify through the DAG.

Chaos Experiments for AI Workflows

Provider Outage

Simulate Anthropic API being unavailable. Does your workflow failover to OpenAI?

Inject: provider: anthropic → status: 503 Expect: Automatic switch to openai provider

Latency Spike

Add 10s delay to model responses. Do agents timeout gracefully?

Inject: latency: +10000ms Expect: Timeout triggers, fallback model activates

Token Budget Exhaustion

Force agents to hit token limits mid-reasoning. Is context preserved?

Inject: maxTokens: 100 Expect: Graceful truncation, no data loss

Hallucination Injection

Inject known-bad outputs. Does SHAKA detect the collapse risk?

Inject: output: randomized garbage Expect: Epistemic signals trigger, output rejected

Rate Limit Hammer

Overwhelm with concurrent requests. Does backoff work?

Inject: concurrency: 100x normal Expect: Exponential backoff, queue management

Network Partition

Disconnect mid-workflow. Is state recoverable?

Inject: network: disconnect after task 2 Expect: Checkpoint restore, resume from last good state

Chaos Engineering Principles

1

Hypothesis-Driven

Define what "normal" looks like. Measure deviation. Every experiment tests a specific resilience hypothesis.

2

Controlled Blast Radius

Start small. Test in staging. Expand scope gradually. Never chaos-test production without guardrails.

3

Automate Everything

Chaos experiments should run continuously. CI/CD pipelines include resilience validation.

4

Learn and Improve

Every failure teaches something. Document findings. Fix weaknesses. Build antifragility.

In Practice: Chaos Mode

chaos-test.nika.yaml
# Chaos mode enabled for resilience testing
chaos:
  enabled: true
  experiments:
    - type: provider_failure
      target: anthropic
      probability: 0.3          # 30% chance of failure

    - type: latency_injection
      target: all
      delay: 5000               # +5s to all requests

    - type: token_exhaustion
      target: agent:analyze
      maxTokens: 500            # Force budget limit

tasks:
  - id: analyze
    agent:
      prompt: "Analyze codebase security"
      model: claude-sonnet-4-5
      fallback:
        - model: gpt-4o
        - model: llama3.2

# Run with: nika run --chaos chaos-test.nika.yaml

Research Status

Chaos engineering for AI is an emerging research area. We're exploring:

  • Chaos mode flag for workflows — In Design
  • Failure injection experiments — In Research
  • SHAKA integration for chaos detection — In Research
  • CI/CD chaos testing pipeline — Future
Declarative Intent Self-Healing Agents