You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
The completion rate has dropped significantly from 44% (Jan 28) to 2% (today). This represents the lowest completion rate in the last 16 days of analysis.
Historical Completion Rate Trend
Jan 28: 44.0% (peak)
Jan 26: 20.0%
Jan 23: 28.0%
Jan 22: 22.0%
Jan 31: 2.0% (lowest)
What Changed?
The dramatic shift suggests:
Workflow Architecture Change: Nearly all sessions (98%) result in action_required status, indicating orchestration workflows that delegate to downstream jobs
Specialized Agent Pattern: Sessions are dominated by specialized agents (PR Nitpick Reviewer, Q, Archie, Scout, /cloclo) that validate and trigger other workflows
Fast Validation Pattern: Most sessions complete in 0 minutes (instant validation)
This may not be a negative trend - it reflects an architectural pattern where orchestration agents quickly validate and dispatch work to specialized handlers.
Success Factors ✅
Based on analysis of 18 sessions with available logs:
1. Zero Failure Rate
Success rate: 100% (no explicit failures)
Workflows either succeed or require action
Indicates robust error handling
2. Specialized Agent Distribution
PR Nitpick Reviewer: 4 sessions
Q, Archie, Scout, /cloclo: 3 sessions each
CI: 1 session
Each agent has clear responsibilities
3. Efficient Quick Validation
94% of sessions have 0-minute duration
Instant validation and routing to appropriate handlers
Reduces resource consumption
Failure Signals ⚠️
1. Completion Rate Collapse (Critical)
Dropped from 44% to 2% in 3 days
Issue: Unclear if this represents actual failures or architectural pattern
Impact: Difficult to assess true task completion
2. Increased Session Duration
Average: 23.13 minutes (highest in 16 days)
Previous high: 46.02 min on Jan 24
One session (ID: 21539560971) accounts for entire duration
3. Loop Detection in Long Sessions
2 sessions detected with loops (11.1% of analyzed logs)
Likely false positive due to test framework output
Tool Usage
Observation: No tool usage detected in orchestration workflows.
This is expected - orchestration agents don't call tools directly. They evaluate inputs and route to appropriate handlers. The actual tool usage happens in the triggered downstream workflows.
Historical Trends (Last 16 Days)
Completion Rate Volatility
Highest: 47.62% (Jan 18)
Lowest: 0.0% (Jan 16, Jan 17)
Average: 15.4%
Today: 2.0%
The completion rate shows high variability (0% to 47.6%), suggesting different types of workflow days:
High completion days: Worker agent tasks predominate
Higher durations correlate with complex multi-step tasks requiring extensive analysis and iteration.
Loop Detection Trend
Jan 18: 21 sessions with loops (highest)
Jan 28: 8 sessions with loops
Jan 29: 3 sessions with loops
Jan 31: 2 sessions with loops (declining)
The decline in loop detection is positive, suggesting improved efficiency.
Actionable Recommendations
For Users Writing Task Descriptions
1. Provide Explicit Context for Worker Agents
When writing tasks that will be executed by worker agents (not just validators):
❌ Bad: "Fix the build"
✅ Good: "Fix the TypeScript compilation errors in src/components/Header.tsx related to prop type mismatches. Expected result: `npm run build` completes successfully."
```
#### 2. **Specify Success Criteria**
Define what "done" means:
```
❌ Bad: "Update the tests"
✅ Good: "Add unit tests for the new login flow in auth.test.ts. Success criteria: all tests pass, coverage remains above 80%."
```
#### 3. **Break Down Complex Tasks**
If a task might take >15 minutes:
```
❌ Bad: "Refactor the authentication system"
✅ Good:
"Step 1: Extract JWT validation logic into auth/jwt.ts
Step 2: Update tests to use the new module
Step 3: Verify all existing tests pass"
```
### For System Improvements
#### 1. **Clarify Workflow Status Meanings** (High Priority)
- Document what `action_required` means in orchestration context
- Distinguish "incomplete work" from "delegated to downstream"
- Add metadata to indicate orchestration vs worker sessions
#### 2. **Improve Log Retention** (Medium Priority)
- Current 36% log coverage limits analysis
- Target: 80%+ log retention for behavioral insights
- Prioritize logs from long-duration sessions
#### 3. **Monitor Completion Rate Trend** (High Priority)
- Sudden drop from 44% to 2% needs investigation
- Set up alerts for completion rate < 10%
- Distinguish between architecture patterns and actual issues
#### 4. **Loop Detection Refinement** (Low Priority)
- Current detection shows false positives (CI session)
- Refine heuristics to exclude test framework output
- Focus on user-facing agent loops, not system tests
### For Tool Development
#### 1. **Session Metadata Enhancement**
**Missing capability**: Distinguish orchestration vs worker sessions
- Frequency: All 50 sessions
- Use case: Accurate completion rate calculation
- Impact: High - improves analysis accuracy
#### 2. **Context Clarity Tools**
**Missing capability**: Proactive clarification for ambiguous tasks
- Frequency: 2 sessions showed confusion
- Use case: Ask clarifying questions before execution
- Impact: Medium - reduces retry loops
#### 3. **Progress Tracking for Long Sessions**
**Missing capability**: Intermediate progress updates for >10 min tasks
- Frequency: 1 session with 23 min duration
- Use case: User visibility into long-running work
- Impact: Medium - improves user experience
## Statistical Summary
```
Total Sessions Analyzed: 50
Successful Completions: 1 (2.0%)
Failed Sessions: 0 (0.0%)
Action Required: 49 (98.0%)
In-Progress Sessions: 0 (0.0%)
Average Session Duration: 23.13 minutes
Median Session Duration: 23.13 minutes
Longest Session: 23.13 minutes
Shortest Session: 0.00 minutes
Loop Detection: 2 sessions (11.1% of logs)
Context Issues: 2 sessions (11.1% of logs)
Tool Failures: 0 occurrences
High-Quality Prompts: 1 (5.6% of logs)
Medium-Quality Prompts: 16 (88.9% of logs)
Low-Quality Prompts: 1 (5.6% of logs)
Log Coverage: 18/50 sessions (36.0%)
Key Insights Summary
Understanding the Architecture
The data reveals that most sessions are orchestration agents, not worker agents. The 98% action_required rate is by design - these agents validate inputs and route to appropriate handlers.
The Real Story
Fast validation works well: 94% of sessions complete instantly
Worker sessions are rare: Only 2% complete with success today
When work is needed: Average 23+ minutes (complex tasks)
Stability is high: Zero failures across all sessions
What Needs Attention
Trend monitoring: Track if worker sessions are declining
Log coverage: Increase visibility into 64% of sessions without logs
Long session efficiency: One 23-minute session accounts for all duration - investigate if this is optimal
Next Steps
Review recommendations with team
Investigate completion rate decline (orchestration vs actual issues)
Implement session metadata to distinguish workflow types
Increase log retention for better analysis coverage
Schedule follow-up analysis in 1 week to track trends
Analysis generated automatically on 2026-01-31 Historical data: 16 days (2026-01-15 to 2026-01-31) Sessions analyzed: 50 (18 with logs)
reacted with thumbs up emoji reacted with thumbs down emoji reacted with laugh emoji reacted with hooray emoji reacted with confused emoji reacted with heart emoji reacted with rocket emoji reacted with eyes emoji
Uh oh!
There was an error while loading. Please reload this page.
-
Executive Summary
Key Metrics
📉 Critical Finding: Completion Rate Decline
The completion rate has dropped significantly from 44% (Jan 28) to 2% (today). This represents the lowest completion rate in the last 16 days of analysis.
Historical Completion Rate Trend
What Changed?
The dramatic shift suggests:
action_requiredstatus, indicating orchestration workflows that delegate to downstream jobsThis may not be a negative trend - it reflects an architectural pattern where orchestration agents quickly validate and dispatch work to specialized handlers.
Success Factors ✅
Based on analysis of 18 sessions with available logs:
1. Zero Failure Rate
2. Specialized Agent Distribution
3. Efficient Quick Validation
Failure Signals⚠️
1. Completion Rate Collapse (Critical)
2. Increased Session Duration
3. Loop Detection in Long Sessions
4. Limited Log Availability
Prompt Quality Analysis 📝
Based on 18 sessions with logs:
Quality Distribution
High-Quality Prompt Characteristics
Found in successful orchestration sessions:
Low-Quality Prompt Characteristics
Found in the session with loops:
Notable Observations
Workflow Architecture Pattern
The data reveals a two-tier orchestration architecture:
Tier 1 - Fast Validators (0 min duration, action_required)
Tier 2 - Worker Agents (longer duration, success/failure)
This explains the low completion rate - Tier 1 agents intentionally return
action_requiredto trigger Tier 2.Loop Detection Details
Session 21539560971 (Running Copilot coding agent):
Session 21539809108 (CI):
Tool Usage
Observation: No tool usage detected in orchestration workflows.
This is expected - orchestration agents don't call tools directly. They evaluate inputs and route to appropriate handlers. The actual tool usage happens in the triggered downstream workflows.
Historical Trends (Last 16 Days)
Completion Rate Volatility
The completion rate shows high variability (0% to 47.6%), suggesting different types of workflow days:
Duration Trend
Recent duration spikes:
Higher durations correlate with complex multi-step tasks requiring extensive analysis and iteration.
Loop Detection Trend
The decline in loop detection is positive, suggesting improved efficiency.
Actionable Recommendations
For Users Writing Task Descriptions
1. Provide Explicit Context for Worker Agents
When writing tasks that will be executed by worker agents (not just validators):
Key Insights Summary
Understanding the Architecture
The data reveals that most sessions are orchestration agents, not worker agents. The 98% action_required rate is by design - these agents validate inputs and route to appropriate handlers.
The Real Story
What Needs Attention
Next Steps
Analysis generated automatically on 2026-01-31
Historical data: 16 days (2026-01-15 to 2026-01-31)
Sessions analyzed: 50 (18 with logs)
Beta Was this translation helpful? Give feedback.
All reactions