Agent Persona Exploration - 2026-02-23

[github-actions[bot]]

This report analyzes the agentic-workflows custom agent's behavior across 8 representative automation scenarios from 5 software worker personas. The agent was evaluated on trigger appropriateness, tool selection, security practices, prompt clarity, and completeness.

⚠️ Methodology Note: The agentic-workflows agent has disable-model-invocation: true and is a GitHub Copilot Chat-only feature. Evaluation was performed by deeply analyzing the agent's prompt files (create-agentic-workflow.md, github-agentic-workflows.md) and scoring expected responses against each scenario. This is a documentation/behavioral analysis rather than live invocation testing.

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Persona Overview

• Agent: agentic-workflows (dispatcher → create-agentic-workflow prompt)
• Scenarios Tested: 8 (across 5 personas: Backend Engineer, Frontend Developer, DevOps Engineer, QA Tester, Product Manager)
• Average Quality Score: 4.08 / 5.0
• Workflow Run: §22289684538

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Score Summary

Dimension	Average
Trigger Appropriateness	⭐ 4.88 / 5.0
Tool Selection	4.00 / 5.0
Security Practices	3.88 / 5.0
Prompt Clarity	3.75 / 5.0
Completeness	3.88 / 5.0

Strongest dimension: Trigger selection (4.88) — the agent almost always maps tasks to correct GitHub Actions triggers.
Weakest dimension: Prompt clarity (3.75) — complex infrastructure scenarios receive less actionable agent instructions.

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Key Findings

• ✅ Trigger inference is a clear strength — all 8 scenarios received ≥4 trigger scores; fuzzy scheduling guidance (schedule: weekly, daily on weekdays) is well-documented and consistently applied
• ✅ Issue automation is the agent's sweet spot — bug triage (4.8) and PM digests (4.6) are near-perfect: correct roles: all guidance for external filers, proper safe-output selection
• ⚠️ Tool selection degrades for infrastructure/browser tasks — scenarios requiring Terraform, Playwright+Storybook, or deployment platform APIs score 3–3.4 on tools/completeness
• ⚠️ Security scoring is inconsistent across domains — excellent for GitHub API patterns (safe-outputs enforcement, minimal permissions) but weaker for external platform integrations where network rules may be under-specified
• 🔍 The workflow_run trigger is under-surfaced — the DevOps-1 scenario (deployment failure → incident) ideally uses workflow_run: completed, but the agent may default to suggesting manual triggers

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Top Patterns

1. Most common trigger: pull_request (3 scenarios), schedule: weekly with auto-workflow_dispatch (3 scenarios), issues: labeled (1 scenario), workflow_run (1 scenario)
2. Most recommended tools: github toolset: [default] (7/8 scenarios), bash (5/8), playwright for browser tasks (1/8)
3. Consistent security wins: Always routes GitHub write ops through safe-outputs, recommends roles: all for public-facing issue workflows, enforces minimal permissions (contents: read)

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View High Quality Responses — Top 3 (Score ≥ 4.6)

🥇 BE-2 — Bug Issue Auto-Triage (4.8/5.0)

• The agent excels here because this is the canonical agentic workflow use case
• Correctly identifies issues: labeled trigger with bug filter
• Proactively suggests roles: all (documented in the prompt) since external users file bugs
• Maps to add-label + add-comment safe-outputs — no direct GitHub API calls
• Instruction: "Classify severity as critical/high/medium/low based on reported impact, apply label, comment with rationale, ping @on-call if critical"

🥈 PM-1 — Weekly Feature Digest (4.6/5.0)

• Fuzzy scheduling is applied perfectly: schedule: weekly → compiler scatters time + adds workflow_dispatch
• github toolset: [issues, pull_requests] is the right minimal toolset
• create-discussion safe-output is ideal for stakeholder-facing content
• Label-based grouping by customer impact maps directly to issue/PR label filters

🥉 BE-1 — PR Schema Change Review (4.2/5.0)

• pull_request trigger with path filter for migration files is well-handled
• github toolset: [default] provides file diff access
• add-comment safe-output posts findings
• Minor gap: the agent may not generate a strongly-structured schema analysis prompt (e.g., explicit checklist for DROP TABLE, missing FK indexes, type coercion risks)

View Areas for Improvement — Bottom 3 (Score ≤ 3.8)

⚠️ DO-2 — Infrastructure Drift Report (3.4/5.0)

• Trigger is correct (schedule: weekly) but tool selection is weak
• Terraform state comparison requires either Terraform Cloud API (web-fetch with auth token), remote state files (S3/GCS), or terraform show via bash — none of these are surfaced by the agent's guidance
• Network rules for Terraform Cloud (app.terraform.io) or cloud storage backends are likely missing
• The resulting prompt would be too vague: "compare Terraform state across environments" without concrete tool calls

⚠️ FE-1 — Visual Regression Report (3.6/5.0)

• playwright tool suggestion is correct for screenshot capture
• Gap: no guidance for launching a Storybook dev server as a pre-step, detecting which stories changed, or diffing images pixel-by-pixel
• network: node inference is a strength (correctly identifies Node.js toolchain needs)
• Security: the workflow needs access to a running Storybook URL — this dependency on a live preview URL may not be addressed

⚠️ FE-2 — Stale Preview Environment Cleanup (3.8/5.0)

• Scheduling is perfect; GitHub Deployments API access via toolset is correctly suggested
• Gap: actual environment teardown is platform-specific (Vercel CLI, Netlify API, Render API) — the agent has no platform-specific tooling guidance
• The agent might suggest a generic bash approach without knowing the specific deployment platform, leaving implementation details as an exercise for the user

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Recommendations

1. Add infrastructure tool guidance to the create prompt — The agent needs Terraform/cloud state comparison patterns. A section on "infrastructure workflows" with Terraform Cloud API, AWS/GCP SDK bash patterns, and required network domains would lift DO-2 from 3.4 to 4+.

2. Improve workflow_run trigger discoverability — The current prompt does not highlight the workflow_run: completed trigger for post-deployment/CI failure scenarios. Adding an explicit example for "when another workflow fails → create incident" would improve DevOps scenarios significantly.

3. Add Playwright+framework integration patterns — Visual regression testing requires more than just playwright: {} — it needs Storybook launch steps, story discovery via bash, and image diff tooling. A mini-guide or example workflow for visual regression would address the FE persona gaps.

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References:

• §22289684538

AI generated by Agent Persona Explorer

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