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Specialized Agents
🌍 English narrative is auto-generated on release. See the French version for the current full descriptions.
Beyond the main pipeline (Planner / Coder / QA), WorkPilot AI ships a library of specialized agents invokable from the Kanban, command palette, or CLI. The list below is regenerated automatically from apps/backend/prompts/.
Auto-generated from apps/backend/prompts/ · 55 agents.
Beyond the core Planner → Coder → QA Reviewer/Fixer pipeline that drives every build, WorkPilot AI ships a set of specialized agents that handle narrower jobs — either standalone or as subagents spawned by the Coder during implementation.
| Agent | Role |
|---|---|
| Planner | Complexity assessment, phased subtask breakdown, dependency analysis |
| Coder | Context-aware implementation with parallel subagent spawning |
| QA Reviewer / Fixer | Acceptance criteria validation and automated issue resolution |
| Test Generator | Unit and integration test generation, streaming code live as it's written |
| Flaky Test Detective | Parses test reports (JUnit XML, .NET TRX, …) to surface intermittently failing tests |
| Refactorer | Safe code refactoring with pattern detection and API migration |
| Documenter | README, API docs, and architecture documentation generation |
| Migration Agent | Framework and library migration with breaking change detection |
| Release Coordinator | Orchestrates release readiness, changelog, and version bumps |
| Memory Manager | Graphiti-based knowledge graph management across sessions |
These agents are backed by a growing library of task-specific prompts under apps/backend/prompts/ — covering ideation (performance, security, code quality, UI/UX), incident response, architecture review, and more — so each specialized task gets a purpose-built prompt rather than a generic one.
All agent work happens inside isolated git worktrees, keeping the main branch untouched until a human reviews and merges the result. For workloads that benefit from running several agents at once, WorkPilot AI supports up to 12 parallel agent terminals, along with a Mission Control dashboard for monitoring live status, token consumption, and per-agent reasoning across the fleet. Higher-level orchestration features — like the Consensus Arbiter, which reconciles conflicting findings from security scans and QA reviewers, and Agent Coach, which suggests cheaper models or better effort levels based on real usage — sit on top of this agent layer to keep multi-agent runs coherent and cost-aware.
➡️ Continue: Integrations
Getting started / Pour débuter
- 🏠 Home
- 📘 Introduction · fr
- ⚡ Installation · fr
- 🚀 First project · fr
- 🧠 Key concepts · fr
- ❓ FAQ · fr
Usage
- 🖥 User interface · fr
- 📝 Creating a spec · fr
- 🔁 Multi-agent pipeline · fr
- 🤖 Specialized agents · fr
- 🔌 Integrations · fr
- 💡 AI providers · fr
- 🧩 Memory system · fr
Advanced / Avancé
Community / Communauté