Workflow Optimizer

Analyze Claude Code workflows using the Agent Monitor's workflow intelligence engine.

Input

The user provides: $ARGUMENTS

Options: "analyze", a session ID for single-session analysis, or a focus: "tools", "subagents", "cost", "errors".

Data Sources

Endpoint	Returns
GET /api/sessions?limit=100	Session list with metadata
GET /api/workflows/{sessionId}	11 workflow datasets (see below)
GET /api/analytics	Tool usage top 20, event types, agent types
GET /api/pricing	Model pricing rules for cost comparison

Workflow Intelligence API (GET /api/workflows/{sessionId})

Returns these 11 datasets per session:

Dataset	Content
stats	Aggregate session stats: tool count, agent depth, event count
orchestration	DAG: agent nodes with parent/child edges, depths, types
toolFlow	Transition matrix: tool A → tool B with counts (common sequences)
effectiveness	Subagent success: per-type completion rates, avg duration, task success
patterns	Recurring sequences: detected workflow patterns with frequency
modelDelegation	Model choices: which models are delegated which tasks
errorPropagation	Error flow by depth: where in the agent tree errors originate and propagate
concurrency	Concurrency lanes: overlapping agent execution timelines
complexity	Complexity score: numerical score based on depth, breadth, tool diversity
compaction	Compaction impact: token savings, frequency, context health
cooccurrence	Agent pairs: which agents frequently run together

Optimization Analyses

1. Tool Flow Optimization

From toolFlow transition data:

• Identify the most common tool sequences (e.g., Read → Edit → Bash)
• Find redundant transitions (same tool called repeatedly = retries)
• Detect anti-patterns: high-frequency failure loops
• Recommend tool chain shortcuts

2. Subagent Strategy

From effectiveness + orchestration:

• Which subagent types (task, explore, code-review) have highest completion rates
• Average duration per subagent type — are subagents taking too long?
• Underutilized types: tasks that could benefit from delegation
• Over-spawning: too many subagents for simple tasks

3. Model Delegation Analysis

From modelDelegation:

• Which models handle which task types
• Cost-per-task comparison across models
• Opportunities to delegate simple tasks to cheaper models (Haiku/Sonnet instead of Opus)
• Calculate estimated savings from model rebalancing

4. Error Prevention

From errorPropagation:

• Where errors originate (agent depth level)
• How errors cascade to parent agents
• Error types (APIError, tool failure) by frequency
• Defensive strategies: which patterns lead to fewer errors

5. Concurrency Optimization

From concurrency:

• Which agents run in parallel vs sequential
• Bottlenecks: sequential agents that could be parallelized
• Resource contention: overlapping heavy tasks

6. Context Health

From compaction:

• How often compaction occurs per session
• Token recovery from compaction baselines
• Sessions that hit context limits — suggest breaking into smaller tasks

Output

Prioritized recommendations table:

#	Recommendation	Source Data	Impact	Effort	Est. Savings

Top 5 recommendations with detailed explanation, supporting data from the workflow API, and implementation steps.