DAG Mutation Strategist

Decides HOW to mutate a DAG when things go wrong. Given a failure diagnosis from dag-quality or dag-ops, selects the optimal recovery strategy.

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When to Use

✅ Use for:

• Choosing a mutation strategy after a node fails
• Deciding between retry, replan, fork, or escalate
• Adapting to mid-execution discoveries that change the plan
• Cost-aware recovery (don't spend $0.50 recovering a $0.01 failure)

❌ NOT for:

• Detecting failures (use dag-quality)
• Executing the mutation (use dag-planner to rewire, dag-runtime to execute)
• General error handling in code

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Strategy Selection

flowchart TD
  F[Failure detected] --> T{Failure type?}
  
  T -->|Transient: API timeout, rate limit| R[Retry with backoff]
  T -->|Model: wrong format, refusal| M{Budget allows upgrade?}
  M -->|Yes| U[Replace with stronger model]
  M -->|No| P[Rephrase prompt, same model]
  
  T -->|Contract: output schema mismatch| C[Retry with explicit schema in prompt]
  T -->|Quality: below threshold| Q{Iteration count?}
  Q -->|< max| L[Loop back with feedback from dag-quality]
  Q -->|≥ max| E{Challenger skill available?}
  E -->|Yes| SW[Swap to challenger skill]
  E -->|No| HG[Escalate to human gate]
  
  T -->|Logic: wrong approach entirely| D{Cost of replan vs remaining budget?}
  D -->|Replan < 30% of budget| RP[Replan affected subgraph]
  D -->|Replan > 30%| HG
  
  T -->|Cascade: upstream caused this| FIX[Fix root cause node first]
  FIX --> R

Strategy Catalog

Strategy	When	Cost	Risk
Retry with backoff	Transient failures (timeout, rate limit)	Same as original (~$0.001-0.01)	Low — usually works in 1-3 retries
Rephrase prompt	Model refused or misunderstood	Same as original	Medium — may not fix the root cause
Upgrade model	Cheap model failed on complex task	+$0.01-0.10	Low — stronger model usually succeeds
Downgrade + simplify	Expensive model failed, budget tight	-$0.01-0.10	Medium — simpler approach may miss nuance
Inject schema	Output didn't match contract	Same as original	Low — explicit schema usually works
Loop with feedback	Quality below threshold	Same as original + eval cost	Medium — may plateau after 2-3 iterations
Swap skill	Current skill isn't working for this task	Same as original	Medium — challenger may or may not be better
Fork parallel paths	Ambiguous situation, multiple valid approaches	2-3x original	Low — pick best result from parallel attempts
Replan subgraph	Wrong approach for this section	Variable (Sonnet call for replanning)	Medium — new plan may also be wrong
Insert validator node	Output needs additional checking	+$0.001	Low — cheap verification step
Escalate to human	All automated strategies exhausted or too risky	Human time	Zero technical risk — but blocks execution

Decision Principles

1. Cheapest effective fix first: Retry before rephrase. Rephrase before model upgrade. Upgrade before replan.
2. Cost-proportional recovery: Don't spend $0.50 recovering a $0.01 failure. If recovery costs more than re-running the entire DAG, re-run.
3. Escalate, don't loop forever: Max 3 iterations of the same strategy. If it hasn't worked by then, escalate to a different strategy or human.
4. Fix root causes, not symptoms: If Node C failed because Node A produced bad input, fixing Node C is treating the symptom. Fix Node A.

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Anti-Patterns

Retry Everything

Wrong: Retrying every failure with the same prompt and model 10 times. Right: Retry once for transient failures. If it fails twice, the problem isn't transient — change strategy.

Expensive Recovery for Cheap Failures

Wrong: Spawning an Opus replan call ($0.10) to recover from a Haiku formatting error ($0.001). Right: Just retry with explicit schema injection ($0.001).

Ignoring Cascade Failures

Wrong: Fixing Node C when Node A was the root cause. Node C will fail again on the next run. Right: Trace backward through dependencies to find the first node that deviated. Fix that one.