Agent Persona Exploration - 2026-02-17

[github-actions[bot]]

Persona Overview

Research Date: 2026-02-17
Agent: agentic-workflows custom agent
Scenarios Tested: 8 (from 5 software personas)
Average Quality Score: 4.05/5.0

Key Findings

1. Dual-mode behavior: Agent operates in two distinct modes - interactive requirement gathering (5/8 scenarios) vs. immediate workflow creation (3/8 scenarios)
2. Highest quality when creating immediately: All 3 immediate-creation responses scored perfect 5.0/5.0, while interactive responses averaged 3.5/5.0
3. Strong security posture: Consistently applies read-only permissions, safe-outputs, strict mode, and minimal network access
4. Comprehensive trigger knowledge: Suggests appropriate triggers including pull_request, workflow_run, deployment_status, schedule, and workflow_dispatch
5. Production-ready workflows: When creating immediately, agent includes sophisticated features like deduplication, cache-memory knowledge bases, and fuzzy scheduling

Top Patterns

Trigger Types:

• PR automation: pull_request with file path filters
• CI monitoring: workflow_run for post-CI analysis
• Scheduled reports: schedule with fuzzy timing (e.g., 2:58 AM)
• Manual execution: workflow_dispatch for on-demand runs
• Deployment tracking: deployment_status for deployment failures

Tool Recommendations:

• GitHub API access via github toolset
• Persistent data via cache-memory
• Safe GitHub writes via safe-outputs
• External resources via web-fetch (when needed)
• Visual testing via playwright (for frontend scenarios)

Security Practices:

• Minimal permissions (read-only by default)
• All write operations through safe-outputs
• Strict mode validation enabled
• Network access restricted to necessary domains
• Workflow expiration dates (stop-after: +1mo)

View High Quality Responses (Top 3)

1. DevOps: Deployment Incident Analyzer (Score: 5.0/5.0)

Scenario: Monitor failed deployments and create incidents with root cause analysis

Agent Response Highlights:

• Triggers: Both deployment_status and workflow_run for comprehensive coverage
• AI Analysis: GPT-5.1-Codex for log parsing and root cause identification
• Classification: Infrastructure vs. Code vs. Hybrid failure categorization
• Deduplication: Searches for similar incidents to prevent duplicate tracking
• Knowledge Base: Uses cache-memory to track patterns across deployments
• Security: Read-only permissions, safe-outputs for issue creation, 15-minute timeout

Why it excelled: Production-ready workflow with sophisticated failure classification, duplicate prevention, and actionable recommendations structured as immediate/short-term/long-term actions.

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2. Product Manager: Weekly Release Notes (Score: 5.0/5.0)

Scenario: Generate weekly release notes from merged PRs grouped by feature area

Agent Response Highlights:

• Trigger: Scheduled every Wednesday at 2:58 AM UTC (fuzzy scheduling)
• GitHub API: Fetches merged PRs from past 7 days
• Grouping: By labels, file paths, and title keywords (feat:, fix:, [CLI])
• AI Curation: Rewrites technical titles to user-facing benefits
• Output: Posts to GitHub Discussions in "announcements" category
• Contributors: Highlights first-time contributors with special recognition

Why it excelled: Complete automation of manual release note curation with professional formatting, contributor attribution, and appropriate scheduling.

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3. QA: Flaky Test Tracker (Score: 5.0/5.0)

Scenario: Identify flaky tests from CI logs and create tracking issues

Agent Response Highlights:

• Multiple Triggers: workflow_run (after CI), workflow_dispatch (manual), schedule (weekly Monday reports)
• Intelligent Detection: Tracks pass rates, timing variance, error patterns to distinguish flaky from consistently broken tests
• Comprehensive Issues: Failure frequency, error messages, stack traces, environmental context
• Knowledge Base: Persistent cache-memory database at /tmp/memory/flaky-tests/
• Deduplication: Updates existing issues instead of creating duplicates
• Recommendations: Immediate, short-term, and long-term fixes for common causes

Why it excelled: Sophisticated multi-trigger setup, persistent knowledge base, and actionable recommendations that go beyond simple failure reporting.

View Interactive Mode Analysis

Interactive Mode Observations

5 of 8 scenarios triggered interactive requirement gathering rather than immediate workflow creation:

1. Backend: Database Migration Review - Asked about trigger type, file patterns, database type, output format, network access
2. Frontend: Visual Regression - Asked about framework, component rendering, baseline storage, network requirements
3. QA: Test Coverage - Asked about testing framework, coverage generation, report format, thresholds, metrics
4. Frontend: Bundle Size - Asked about trigger, build command, thresholds, actions on exceed
5. DevOps: Cloud Cost Audit - Asked about cloud provider, data source, output format, credentials, services, optimization scope

Pattern

Agent enters interactive mode when:

• Scenario requires environment-specific configuration (database type, cloud provider)
• Technical setup details are ambiguous (build commands, file paths)
• Multiple valid approaches exist (trigger types, output formats)

Quality Impact

• Interactive responses averaged 3.5/5.0 (good but not excellent)
• Immediate creation averaged 5.0/5.0 (perfect scores)

Implication

Interactive mode adds friction and delays workflow creation. Users must answer questions before receiving a workflow. This may be appropriate for complex scenarios but could benefit from providing a "default example" workflow alongside questions.

View Areas for Improvement

1. Provide Default Examples in Interactive Mode

Issue: When agent enters interactive mode, users receive questions but no concrete workflow example.

Impact: Increases friction, requires back-and-forth interaction, delays workflow creation.

Suggestion: Always provide a "starter workflow" with common defaults alongside clarifying questions. Users can use the default or customize based on questions.

Example: For test coverage analysis, provide a default Jest/pytest workflow while asking about specific setup.

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2. Detect Common Patterns for Immediate Creation

Issue: Some scenarios that could have immediate creation (bundle size, test coverage) triggered interactive mode instead.

Impact: Missed opportunities for quick workflow delivery when context is sufficient.

Suggestion: Expand the immediate-creation trigger conditions to include common patterns:

• Bundle size analysis → Default webpack/vite analysis
• Test coverage → Default Jest/pytest with standard thresholds
• Visual regression → Default Playwright screenshot workflow

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3. Inconsistent Workflow Creation Criteria

Issue: Unclear why deployment monitoring triggered immediate creation but cloud cost audit triggered interactive mode. Both involve external data sources and configuration.

Impact: Unpredictable user experience - similar scenarios get different treatment.

Suggestion: Document clear criteria for interactive vs. immediate mode, or always provide both (default workflow + customization questions).

Recommendations

1. Hybrid approach for all scenarios: Provide a working default workflow immediately, followed by customization questions. This gives users a fast path (use defaults) and a thorough path (answer questions for customization).

2. Expand immediate-creation patterns: Add common defaults for bundle size analysis (webpack/vite), test coverage (Jest/pytest), and visual regression (Playwright) to reduce interactive friction.

3. Document workflow creation heuristics: Make it clear to users when they'll get immediate workflows vs. interactive questioning, so they can adjust their initial prompt to trigger the desired behavior.

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References:

• §22082649529 - Current workflow run

AI generated by Agent Persona Explorer