State management, checkpoint architecture, and recovery strategy selection for WinDAGs V3 execution infrastructure. Teaches the validated state machine (transition matrix with fail-fast on illegal transitions), checkpoint-resume protocol (pluggable storage, auto-save, selective re-execution), recovery strategy selection (6 strategies classified by error type and escalated by cost), and why idempotent re-execution beats saga compensation for LLM agent orchestration. Activate on "resilience", "checkpoint", "recovery", "state machine", "resume execution", "idempotent", "storage adapter", "auto-save", "wave checkpoint", "phase boundary", "crash recovery", "durable execution". NOT for failure classification and DAG mutation (use windags-mutator), pre-execution risk scanning (use windags-premortem), quality evaluation (use windags-evaluator), or DAG topology design and execution engine selection (use windags-architect).
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State management, checkpoint architecture, and recovery strategy selection for WinDAGs V3 execution infrastructure. Teaches the validated state machine (transition matrix with fail-fast on illegal transitions), checkpoint-resume protocol (pluggable storage, auto-save, selective re-execution), recovery strategy selection (6 strategies classified by error type and escalated by cost), and why idempotent re-execution beats saga compensation for LLM agent orchestration. Activate on "resilience", "checkpoint", "recovery", "state machine", "resume execution", "idempotent", "storage adapter", "auto-save", "wave checkpoint", "phase boundary", "crash recovery", "durable execution". NOT for failure classification and DAG mutation (use windags-mutator), pre-execution risk scanning (use windags-premortem), quality evaluation (use windags-evaluator), or DAG topology design and execution engine selection (use windags-architect).
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metadata
{"tags":["windags","resilience","checkpoint","recovery","state-machine","crash-recovery"],"pairs-with":[{"skill":"windags-architect","reason":"Architect designs the execution engine that resilience protects. Resilience is the infrastructure layer beneath the engine."},{"skill":"windags-mutator","reason":"Mutator handles DAG-level failure response (topology mutation). Resilience handles node-level state/checkpoint/recovery. Different abstraction levels."},{"skill":"windags-evaluator","reason":"Evaluator quality scores feed into recovery strategy selection. Low grades trigger model escalation."},{"skill":"windags-premortem","reason":"PreMortem identifies risks before execution. Resilience mitigates those risks during execution."},{"skill":"windags-decomposer","reason":"Decomposer creates the wave structure that resilience uses for checkpoint boundaries."}]}
WinDAGs Resilience
The execution infrastructure that makes WinDAGs recoverable. Three pillars: validated state management (preventing illegal transitions), checkpoint persistence (enabling resume from any wave boundary), and intelligent recovery (selecting the right strategy for each error class).
The core insight: LLM agent outputs are non-deterministic and expensive. Idempotent re-execution of failed nodes while preserving completed results is cheaper and more reliable than saga-based compensation.
When to Use
Use for:
Designing state machines for DAG node lifecycles
Implementing checkpoint persistence with pluggable storage
Building resume protocols that preserve completed work
Selecting recovery strategies based on error classification
Adding auto-save intervals to long-running executions
Implementing phase boundary checkpoints for multi-phase DAGs
Deciding between idempotent re-execution and saga compensation
NOT for:
Failure classification and DAG mutation at runtime (use windags-mutator)
Pre-execution risk scanning and failure timing analysis (use windags-premortem)
Quality evaluation of node outputs (use windags-evaluator)
DAG topology design and execution engine selection (use windags-architect)
Learning updates and skill quality tracking (use windags-curator)
Core Insight: Idempotent Re-execution vs. Saga Compensation
flowchart TD
subgraph saga["Saga Compensation"]
S1["Node A completes"] --> S2["Node B fails"]
S2 --> S3["Compensate Node A\nReverse its effects"]
S3 --> S4["Problem: LLM output is\nnon-deterministic.\nCompensation destroys\na good result."]
end
subgraph idem["Idempotent Re-execution"]
I1["Node A completes"] --> I2["Node B fails"]
I2 --> I3["Preserve Node A result\nRe-execute Node B only"]
I3 --> I4["Node A output unchanged.\nNode B gets fresh attempt\nwith same inputs."]
end
Why idempotent re-execution wins for LLM orchestration:
Non-determinism: Re-running Node A produces different text. Compensation means losing a good result you cannot reproduce.
Cost: Compensation requires undoing work that cost tokens, then redoing it. Re-execution of just the failed node skips all that.
Bounded side effects: In WinDAGs, node outputs are the primary artifact. There are no external database mutations to reverse.
Checkpoints make it free: Completed results are already persisted. Resume is "skip completed, retry failed."
When saga compensation IS appropriate: The mutator's 7 mutation types (add_node, remove_node, replace_node, etc.) DO use saga compensation because they modify the DAG structure itself, not individual node outputs. Topology changes are a different abstraction level — removing a node and its edges is a reversible structural operation. Individual node re-execution is not.
State Machine Design
Every node in a WinDAGs execution passes through a validated state machine. The transition matrix is enforced at runtime — illegal transitions throw immediately rather than corrupting state silently.
failed → ready (not failed → running): Forces the node through the ready-check again, verifying dependencies are still valid before retry. A dependency may have been cancelled while this node was failing.
completed is terminal: No re-execution through state transitions. To re-execute a completed node, use the checkpoint resume protocol with reExecuteCompleted: true. This is a deliberate choice — accidental re-execution of expensive completed nodes is the most common resilience bug.
skipped propagates deterministically: If any dependency fails or is skipped, downstream nodes skip. No partial execution of a dependency chain.
Fail-fast on illegal transitions: Throw InvalidStateTransitionError, don't silently ignore. Silent corruption of the state machine is worse than a crash.
BC-RESIL-001: All state transitions MUST pass through transition validation. Direct state assignment bypassing the matrix is prohibited. Every transition emits an event to listeners (for logging, quality tracking, cost accounting).
Checkpoint Architecture
A checkpoint captures the minimum state needed to resume execution from any wave boundary.
What to Persist
Checkpoint {
id: dagId-executionId-timestamp
dag: node IDs, dependencies, configs (minimal structure)
execution: node states, final outputs, errors, current wave, token usage
version: schema version for forward compatibility
metadata: phase info, project context
}
What NOT to persist: Full conversation histories, intermediate LLM turns, model weights, raw prompts. Only structural state and final outputs. Checkpoints should be <1MB, not 50MB.
Storage Adapter Pattern
flowchart TD
CM["CheckpointManager"] --> SA["StorageAdapter interface"]
SA --> MEM["MemoryAdapter\nTesting, ephemeral"]
SA --> LS["LocalStorageAdapter\nBrowser, 5MB limit"]
SA --> FS["FileStorageAdapter\nNode.js, .dag-checkpoints/"]
SA --> CUSTOM["Custom adapters\nRedis, S3, Supabase, SQLite"]
The interface is simple: get, set, delete, has, keys, clear, getStats. Any backend that implements these 7 methods works as a checkpoint store. Auto-detection picks the right adapter for the environment.
When to Checkpoint
Trigger
Required?
Rationale
Wave completion
Mandatory
Natural synchronization point. All nodes in wave are terminal.
Phase boundary
Mandatory
Multi-phase DAGs need coarse-grained recovery points.
Before human gates
Recommended
Humans are slow. Crash during wait = lost progress.
Before expensive nodes
Recommended
Opus-tier nodes cost tokens. Checkpoint before, not after.
Auto-save interval
Optional
Configurable timer for long-running waves.
Cleanup
maxCheckpointsPerDAG (default: 10). Oldest-first deletion. Prevents storage bloat in production systems that run thousands of DAGs.
BC-RESIL-002: Checkpoints MUST be saved at every wave boundary and every phase boundary. The minimum checkpoint frequency is once per wave completion. Within a wave, checkpointing is optional.
Resume Protocol
Resume is where idempotent re-execution becomes concrete. The protocol determines which nodes to re-execute and which to preserve.
flowchart TD
LOAD["Load checkpoint"] --> PREPARE["prepareResume(options)"]
PREPARE --> EACH{"For each node"}
EACH --> COMPLETED{"completed?"}
COMPLETED -->|"default"| PRESERVE["Preserve result\nSkip re-execution"]
COMPLETED -->|"reExecuteCompleted: true"| REEXEC["Mark for\nre-execution"]
EACH --> FAILED{"failed?"}
FAILED -->|"retryFailed: true (default)"| REEXEC
FAILED -->|"retryFailed: false"| SKIP["Leave as failed"]
EACH --> PENDING{"pending / ready / running?"}
PENDING --> REEXEC
EACH --> SPECIFIC{"In reExecuteNodes list?"}
SPECIFIC --> REEXEC
The resume output: nodesToExecute (set), completedResults (preserved map), startFromWave (calculated from node states), previousTokenUsage (accumulated).
Wave Recalculation
The executor recalculates which wave to start from based on which nodes need execution. If waves 0-2 are fully completed and wave 3 has failures, execution resumes at wave 3. Waves 0-2 are never re-entered.
BC-RESIL-003: When resuming, completed node results MUST be injected into the execution state before any node begins. Downstream nodes that depend on completed nodes must receive the preserved outputs, not null. Failure to inject results produces cascading MISSING_DEPENDENCY errors.
Recovery Strategy Selection
When a node fails, the RecoveryManager classifies the error and selects a recovery strategy. Strategies escalate by cost — cheap retries first, expensive interventions last.
How this differs from the mutator's escalation ladder: Recovery operates on a single node — retry it, improve its prompt, upgrade its model. The mutator operates on the DAG topology — add nodes, remove edges, restructure waves. Recovery is the first responder; the mutator is called when recovery fails and the DAG itself needs surgery.
BC-RESIL-004: Recovery strategies MUST be attempted in escalating cost order. retry-same before retry-with-feedback before escalate-model. Do not jump to an expensive strategy when a cheap one has not been tried. Each attempt is logged with strategy name, cost, and outcome.
Wave-Based Execution
Nodes are organized into topological waves. Within a wave, nodes execute in parallel (bounded by maxParallelism). Between waves, the system checkpoints and synchronizes.
Why waves, not greedy scheduling?
Greedy scheduling executes any node whose dependencies are met, immediately. Maximum parallelism, but:
No natural checkpoint boundaries — where do you save state mid-execution?
Unpredictable failure blast radius — a failure mid-stream affects an unknown number of in-flight nodes
Cannot resolve vague nodes — deferred planning needs completed wave results before the next wave's nodes are finalized
Wave scheduling accepts slightly lower parallelism for predictable recovery points. Each wave boundary is a synchronization barrier where checkpoints, vague-node resolution, and progress reporting happen.
Backpressure: If a wave has 12 nodes and maxParallelism=4, the executor runs 3 sequential batches of 4. This prevents overwhelming LLM API rate limits and keeps cost predictable per batch.
BC-RESIL-005: Wave transitions are synchronization barriers. All nodes in wave N must reach a terminal state (completed, failed, skipped, or cancelled) before any node in wave N+1 begins. No early starts.
Phase Boundaries
Multi-phase DAGs (RESEARCH → PLAN → BUILD → RELEASE) add a coarser checkpoint layer above waves.
The phase orchestrator:
Executes each phase as a separate DAG with its own waves
Saves a checkpoint at every phase boundary
Passes accumulated outputs from completed phases to the next phase's DAG generator
Each phase can dynamically generate its DAG based on prior results
A failed BUILD phase resumes from the BUILD boundary. RESEARCH and PLAN outputs are preserved in the checkpoint — no re-execution.
Relationship to wave checkpoints: Phase boundaries are coarser-grained. A phase contains multiple waves. Wave checkpoints enable intra-phase recovery. Phase checkpoints enable inter-phase recovery.
Anti-Patterns
The Eager Compensator
Novice
Implements saga compensation for individual node failures, rolling back completed nodes to "redo" them with corrected upstream context
Expert
Preserves completed results, re-executes only the failed node. Uses resume with retryFailed: true, reExecuteCompleted: false.
Timeline
Novice wastes 60-80% of tokens re-executing successful nodes and gets different (possibly worse) results. Expert preserves good work and spends tokens only on the failure.
The Greedy Scheduler
Novice
Launches every ready node immediately, maximizing parallelism, no wave boundaries
Expert
Uses wave-based execution with checkpoint boundaries, accepting slightly lower parallelism for predictable recovery
Timeline
Novice's execution crashes mid-DAG with 15 in-flight nodes and no checkpoint. Restarts from scratch. Expert resumes from wave 3 of 7, preserving 40% of completed work.
The Monolithic Checkpoint
Novice
Serializes the entire execution state — conversation histories, intermediate LLM turns, full prompts, raw tool outputs — into every checkpoint
Expert
Checkpoints only structural state: node statuses, final outputs, wave position, token counts. Loads reference data on demand during resume.
Timeline
Novice's checkpoints are 50MB+ each. Storage fills up after 20 DAGs. Expert's checkpoints are <1MB. Can store thousands.
The Optimistic Skipper
Novice
Node A completed, so its output must be fine — pass it downstream without validation
Expert
Validates every completed node's output against the downstream node's input contract at the wave boundary before marking dependents as ready
Timeline
Novice gets cascading byzantine failures where subtly wrong outputs propagate through 4 waves before anyone notices. Expert catches the mismatch at wave 1 and triggers recovery on Node A.
Behavioral Contracts
Contract
Requirement
Enforcement
BC-RESIL-001
All state transitions validated against transition matrix
isValidTransition() check; InvalidStateTransitionError on violation
BC-RESIL-002
Checkpoint at every wave boundary and phase boundary
Mandatory save after wave completion and at phase boundary events
BC-RESIL-003
Resumed executions inject preserved completed results before starting
Resume protocol populates completed results; executor restores before wave loop
BC-RESIL-004
Recovery strategies attempted in escalating cost order
Strategy selector checks attempt count and previously used strategies
BC-RESIL-005
Wave transitions are synchronization barriers
Wave-complete check ensures all nodes terminal before advancing
Reference Files
Consult for deep dives — NOT loaded by default.
File
Consult When
references/state-machine-implementation.md
Implementing the state manager: transition validation, event emission, wave management, snapshot/restore, cascading skip propagation
references/checkpoint-storage-adapters.md
Building storage adapters, auto-save implementation, checkpoint cleanup, version migration, serialization patterns
references/recovery-strategy-details.md
Full recovery loop, error classification logic, feedback augmentation, model escalation paths, decomposition delegation, cost tracking
