Pattern Detect

Identify recurring patterns using the Agent Monitor's workflow intelligence engine.

Input

The user provides: $ARGUMENTS

Options: "all", "tools", "errors", "workflows", "last N sessions".

Data Sources

Endpoint	Returns
GET /api/sessions?limit=200	Session list with status, model, cwd, metadata
GET /api/analytics	tool_usage top 20, event_types, agent_types
GET /api/workflows/{sessionId}	11 datasets per session (see below)

Workflow datasets used for pattern detection

Dataset	Pattern insight
toolFlow	Tool transition matrix: tool A → tool B with counts — reveals sequential habits
patterns	Detected workflow patterns: recurring sequences with frequency scores
cooccurrence	Agent co-occurrence: which agents frequently run together
modelDelegation	Model habits: which models are chosen for which task types
errorPropagation	Error patterns: where errors start and how they cascade by agent depth
effectiveness	Subagent patterns: which types succeed most, avg duration per type
compaction	Compaction triggers: what causes context overflow
complexity	Complexity patterns: session complexity scores over time

Pattern Categories

1. Tool Chain Patterns (from toolFlow)

• Most common sequences: Top 10 tool transitions (e.g., Read → Edit: 145 times)
• Starter tools: First tool used in sessions (indicates task type)
• Finisher tools: Last tool before Stop event
• Anti-patterns: Tool → same Tool repeated (retries/failures)
• Co-occurrence: Tools that always appear together in sessions

2. Workflow Patterns (from patterns)

• Named patterns: Workflow sequences the API has detected with frequency
• Session archetypes: Common session shapes (short edit, long debug, subagent-heavy)
• Project-specific: Patterns that appear in specific working directories

3. Error Patterns (from errorPropagation + event_types)

• Error origins: Which agent depth level produces most errors
• Cascade patterns: Errors that trigger chains of follow-up errors
• APIError frequency: quota hits, rate_limit, overloaded — by time of day
• Recovery patterns: How errors are typically resolved (tool retry vs agent switch)

4. Agent Patterns (from cooccurrence + effectiveness)

• Agent pairs: Which agents are spawned together frequently
• Delegation patterns: Main agent → subagent task delegation habits
• Success by type: Which subagent types (task/explore/code-review) work best for which tasks

5. Temporal Patterns (from session timestamps + daily_sessions)

• Peak hours: When sessions cluster
• Duration patterns: Short vs long session distribution
• Day-of-week trends: Productive days vs quiet days

Output

Pattern Report with top 10 patterns ranked by frequency × impact:

• Pattern name and description
• Frequency (occurrences across analyzed sessions)
• Impact: positive (reinforce), negative (eliminate), or neutral (observe)
• Actionable recommendation for each