Design Phase

Graceful Degradation

AI workflows that bend under pressure — but never break.

"Assume failure. Design for recovery."

In production, things fail. APIs timeout. Models hallucinate. Rate limits hit. Graceful degradation ensures your workflow keeps running, even if not at full capacity.

Why AI Workflows Fail Hard

Traditional AI orchestration treats failures as exceptions. But in agentic systems:

Cascading Failures

One agent fails → dependent agents stall → entire workflow crashes.

Single Points of Failure

One provider outage → all workflows stop. No redundancy.

All-or-Nothing

Either perfect results or complete failure. No middle ground.

Cost Explosions

Infinite retries on expensive models burn through budgets.

Four Pillars of Graceful Degradation

Model Fallback

When Claude Opus fails, fall back to Sonnet. When Sonnet fails, try Haiku. Always have a cheaper, faster backup.

claude-opus-4 → claude-sonnet-4-5 → claude-haiku

Provider Switching

If Anthropic API is down, route to OpenAI. If OpenAI is slow, try Gemini. Multi-provider redundancy built-in.

anthropic → openai → google → ollama

Scope Reduction

Under pressure, reduce agent scope from full to minimal. Less context = faster recovery, lower cost.

full → default → minimal

Task Simplification

Complex multi-step task failing? Break it down. Run simpler sub-tasks. Aggregate partial results.

analyze-all → analyze-chunk-1, chunk-2, chunk-3

The 4-Level Degradation Ladder

Nika implements progressive degradation — start with minimal intervention, escalate only when needed:

L1: Retry

Latency: < 1sImpact: None
  • Immediate retry with same config
  • Exponential backoff
  • Jitter to avoid thundering herd

L2: Adapt

Latency: 1-5sImpact: Minor quality reduction
  • Switch to faster model
  • Reduce context window
  • Trim non-essential tools

L3: Fallback

Latency: 5-30sImpact: Moderate quality reduction
  • Switch provider entirely
  • Use cached response
  • Run simplified workflow

L4: Degrade

Latency: > 30sImpact: Functionality reduced
  • Return partial results
  • Skip optional steps
  • Escalate to human

In Practice: Resilient Workflows

resilient-workflow.nika.yaml
providers:
  anthropic:
    type: anthropic
    priority: 1                # Primary provider
  openai:
    type: openai
    priority: 2                # First fallback
  ollama:
    type: ollama
    priority: 3                # Local fallback (always available)

tasks:
  - id: analyze
    agent:
      prompt: "Deep security analysis"
      model: claude-opus-4
      fallback:
        - model: claude-sonnet-4-5   # L2: Cheaper model
        - model: gpt-4o              # L3: Different provider
        - model: llama3.2            # L4: Local, always works
      retry:
        maxAttempts: 3
        backoff: exponential
      timeout: 60000

  - id: quick-scan
    agent:
      prompt: "Quick security check, top issues only"
      model: claude-haiku      # Degraded version
      maxTurns: 3

flows:
  - source: analyze
    target: quick-scan
    condition: allFallbacksFailed   # Ultimate fallback

Implementation Status

  • Multi-provider support — Implemented
  • Timeout and retry logic — Implemented
  • Automatic model fallback chains — In Design
  • SHAKA-driven degradation decisions — In Research
Bounded Rationality Declarative Intent