The AI Orchestration Maze: Evaluating Plandex, Roo-Commander, and the Quest for Effective Multi-Agent Systems
As we integrate AI more deeply into development, the need for effective orchestration—managing complex workflows, coordinating multiple AI agents, or even just structuring a sophisticated interaction with a single powerful agent—becomes increasingly apparent. My exploration into AI environments and LLMs naturally led me to investigate tools designed for this very purpose. This post details my findings on several such systems.
Early Ideas: Cost-Saving and System Prompts
The idea of optimizing AI interactions isn’t new. Projects like RooCodeMicroManager hinted at using cheaper models for simpler sub-tasks, a compelling concept for cost efficiency, though it raises questions about context management and rule application across different models.
Inspiration for crafting effective interactions also came from resources like system-prompts-and-models-of-ai-tools (highlighted in this YouTube video), offering valuable hints for system prompt design. However, the AI landscape is volatile; the DMCA takedown of a less-known project like anon-kode served as a stark reminder of the potential fragility of open-source AI tools.
Plandex: Promising Prompts, Practical Problems
Plandex emerged as an interesting contender. After a local Docker installation (which had a few initial hiccups), I was impressed by its underlying system prompts. They were well-crafted and provided a good foundation for structured AI interaction, even influencing my approach to improving chat output verbosity.
However, Plandex also presented challenges:
- Hardcoded Prompts: The excellent system prompts were unfortunately hardcoded, limiting flexibility. For instance, enforcing conventional git commit messages wasn’t straightforward.
- Development Activity: With minimal commits in the current year, mostly from the creator, it felt more like a personal project than a rapidly evolving community tool. This led me to lean towards extracting its valuable ideas for use with more actively developed tools rather than investing in direct contributions to Plandex itself.
- MCP and Provider Limitations: At the time of review, MCP support was missing, and it was limited to OpenAI and OpenRouter providers (with OpenRouter having its own limitations regarding certain models).
Roo-Commander: An Ambitious Attempt with Significant Hurdles
Roo-Commander was another major system I explored, even running a Pacman game creation experiment with it (see the pacman-v11 branch if you’re curious). Initially, it seemed like “RooCodeMicroManager on steroids,” but a deeper dive revealed a very different, and ultimately problematic, architecture. My key critiques include:
- Waterfall on LLMs: It attempts to impose a waterfall-like, document-heavy approach on LLMs, which struggles with the iterative and often unpredictable nature of AI.
- Token Bloat: Interactions started with an enormous ~80k token context, quickly escalating towards 1M tokens even with Gemini 2.5 Pro. This is inefficient and can degrade LLM performance.
- Costly Operations: Heavy reliance on relative paths to load rules and
.rurufiles translates to frequent, costly “tool call” operations for the LLM. - Remote LLM Blindness: Many rules instruct the LLM to list contents of local paths (e.g.,
.ruru/modes/{mode-slug}/kb), which a remote LLM cannot access or understand without specific tooling. - Redundant Mode Loading: System prompts list RooModes that aren’t used; instead, another, larger list of modes is loaded separately.
- Format Overhead: The
toml+mdformat, while potentially useful for pre-compiled rules, is used as the primary format, adding unnecessary system information to the context. - Misapplied Human Processes: Concepts like Scrum and Kanban, or hierarchical human roles (lead, senior, junior), are not effectively applicable to AI agents in this implementation. Role splitting (e.g., Next.js vs. React specialist) also proved problematic.
- Lack of Specs/QA: The system seemed to operate outside the scope of typical software specifications and QA pipelines.
- Documentation and Task Management: The project documentation was very difficult to parse, and task management was overly verbose, neglecting the fact that the core context often resides in an
activeContextfile. Tasks, in my view, should be expanded dynamically, not exhaustively planned upfront.
While ambitious, Roo-Commander’s approach felt misaligned with the strengths and operational realities of current LLMs.
Other Noteworthy Mentions
- Agent-Zero (A0): An amazing visual tool and orchestrator idea, but now largely superseded by Roo’s advancements.
- Composio: A service explored to potentially ease MCP (Model Context Protocol) server installation, though the basic setup process for many MCPs is already quite straightforward.
- Pheromind: Another orchestrator project I became aware of via a YouTube video.
The Path Forward: Still Evolving
The field of AI orchestration is vibrant but still very much in flux. My explorations have revealed powerful ideas but also significant practical challenges in existing tools. The quest continues for systems that are flexible, efficient, and truly enhance the AI-assisted development workflow. For now, a curated list of specialized tools and a keen eye on resources like awesome-mcp-servers seems the most pragmatic approach, alongside exploring promising individual agents like serena, agno, and goose.