Wave Analyzer and Confidence Scorer in AIOX Workflow Intelligence: Complete Technical Guide

The Wave Analyzer and Confidence Scorer are two core components in SynkraAI/aiox-core that optimize workflow execution through parallel wave detection and rank next-step suggestions using a weighted, context-aware scoring algorithm.

AIOX workflow intelligence transforms static workflow definitions into dynamic, optimized execution plans. In the SynkraAI/aiox-core repository, the wave analyzer and confidence scorer in AIOX workflow intelligence work together to determine both how workflows should execute and which next steps are most likely correct based on current session context.

What Is the Wave Analyzer?

The Wave Analyzer identifies opportunities for parallel execution by decomposing workflows into discrete waves—groups of tasks that can run simultaneously without dependency conflicts. Located in .aiox-core/workflow-intelligence/engine/wave-analyzer.js, this engine converts sequential workflow definitions into optimized execution plans.

Dependency Graph Construction and Cycle Detection

The analyzer first constructs a directed acyclic graph (DAG) of task dependencies using buildDependencyGraph(). Before proceeding with optimization, it validates the graph structure by checking for circular dependencies via findCycle(). This ensures that only valid, non-cyclic workflows proceed to the wave analysis stage.

Kahn's Algorithm and Wave Grouping

The core parallelization logic resides in _kahnWaveAnalysis(), which implements Kahn's topological sort algorithm with wave-based grouping. This method produces an ordered array of waves, where each wave contains:

• Tasks eligible for simultaneous execution
• Parallel execution flags
• Estimated durations
• Dependency references

The analyzer calculates optimization gain by comparing sequential versus parallel execution times and identifying the critical path through the workflow.

const {
  WaveAnalyzer,
  createWaveAnalyzer,
} = require('.aiox-core/workflow-intelligence');

// Use the factory (allows lazy loading of the workflow registry)
const analyzer = createWaveAnalyzer();   // ← optional custom taskDurations, registry, etc.

const workflowId = 'story_development';
const result = analyzer.analyzeWaves(workflowId, {
  // optional: supply ad‑hoc task definitions
  // customTasks: [...]
});

console.log('Detected waves:', result.waves);
console.log('Optimization gain:', result.optimizationGain);

What Is the Confidence Scorer?

While the Wave Analyzer optimizes execution structure, the Confidence Scorer determines which workflow suggestions to prioritize. Implemented in .aiox-core/workflow-intelligence/engine/confidence-scorer.js, this component assigns a normalized confidence score (0-1) to each potential next action based on current session context.

The Four Partial Scoring Dimensions

The scorer evaluates suggestions against session context through four weighted dimensions:

1. Command match – Measures exact or partial alignment between the suggestion's trigger command and the user's last command
2. Agent match – Calculates progress through the expected agent sequence (e.g., @dev → @qa → @pm)
3. History depth – Analyzes overlap between recent command history and the suggestion's key commands, applying a recency bonus
4. Project-state match – Provides a neutral fallback score when project state data is ambiguous or unknown

Weighted Normalization

Each dimension contributes to the final score according to configurable SCORING_WEIGHTS constants. The weighted sum is normalized to a 0-1 range, allowing the CLI to rank suggestions deterministically.

const {
  ConfidenceScorer,
} = require('.aiox-core/workflow-intelligence');

const scorer = new ConfidenceScorer({
  // optionally override default weights
  // weights: { COMMAND_MATCH: 0.5, AGENT_MATCH: 0.2, HISTORY_DEPTH: 0.2, PROJECT_STATE: 0.1 }
});

// Example suggestion generated by the suggestion engine
const suggestion = {
  trigger: 'run-tests',
  agentSequence: ['@dev', '@qa', '@pm'],
  keyCommands: ['run-tests', 'review-qa'],
};

// Current CLI session context
const context = {
  lastCommand: 'run-tests',
  lastCommands: ['write-tests', 'run-tests'],
  agentId: '@qa',
  projectState: { /* … */ },
};

const confidence = scorer.score(suggestion, context);
console.log(`Confidence for suggestion: ${(confidence * 100).toFixed(1)}%`);

Integration with AIOX Execution Layer

These components integrate directly with AIOX's execution and suggestion infrastructure. The WaveAnalyzer feeds into .aiox-core/core/execution/wave-executor.js to drive parallel task execution, while the ConfidenceScorer is consumed by .aiox-core/workflow-intelligence/engine/suggestion-engine.js to rank recommendations before CLI presentation.

Both classes are exported from .aiox-core/workflow-intelligence/index.js (lines 53-55), making them available via require('.aiox-core/workflow-intelligence').

Summary

• The Wave Analyzer uses graph theory and Kahn's algorithm to detect parallel execution opportunities in .aiox-core/workflow-intelligence/engine/wave-analyzer.js
• Confidence Scorer applies four weighted dimensions (command match, agent match, history depth, project state) to rank suggestions in .aiox-core/workflow-intelligence/engine/confidence-scorer.js
• Together, they enable the AIOX CLI to present optimized, context-aware workflow guidance
• Both components are accessible through the main workflow-intelligence module exports

Frequently Asked Questions

How does the Wave Analyzer detect circular dependencies in AIOX workflows?

The analyzer runs findCycle() on the dependency graph constructed by buildDependencyGraph() before executing wave analysis. This validation ensures only valid directed acyclic graphs (DAGs) proceed to the _kahnWaveAnalysis() stage, preventing infinite loops during execution.

Can I customize the confidence scoring weights in AIOX?

Yes. The ConfidenceScorer constructor accepts an optional weights object that overrides the default SCORING_WEIGHTS constants. This allows you to adjust the relative importance of command matching, agent sequence alignment, history depth analysis, and project state matching to fit your specific workflow requirements.

What algorithm does the AIOX Wave Analyzer use for parallelization?

The analyzer implements Kahn's topological sort algorithm with wave grouping via the internal _kahnWaveAnalysis() method. This algorithm determines which tasks can execute simultaneously without dependency conflicts while maintaining the correct execution order across waves.

Where are the Wave Analyzer and Confidence Scorer exported in the codebase?

Both are exposed through .aiox-core/workflow-intelligence/index.js at lines 53-55, allowing imports via require('.aiox-core/workflow-intelligence') as WaveAnalyzer, createWaveAnalyzer, and ConfidenceScorer.

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    "instagit": {
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}