As multi-agent systems evolve around LLMs, Routing Agents (R-Agents)—often referred to as Planner Agents—are increasingly adopted as orchestrators. However, most current implementations suffer from critical structural limitations that prevent reliable alignment, auditing, or large-scale deployment.
1️⃣ Misunderstood Intent ≠ True Task Structuring
- R-Agents typically rely on decomposing user prompts into subtasks using natural language.
- These subtasks often lack structural definition or intent preservation mechanisms.
- There’s no guarantee that subtasks faithfully carry forward the original goal or scope.
📌 Consequence:
- Conflicting or redundant subtasks;
- Planner cannot verify decomposition integrity;
- User intent becomes diluted or distorted over steps.
2️⃣ Path Collapse Risk: No Execution-State Continuity
- Most implementations use a “plan once → dispatch all → collect results” model.
- There is no persistent state tracking or feedback loop between planner and executors.
- Failure in any sub-agent does not propagate clearly, often creating a “false completion illusion.”
📌 Consequence:
- No rollback or audit possible;
- Planner operates in a black box;
- System appears stable, while internals may be chaotic.
3️⃣ No Role-Based Boundaries: All Agents Act as Admins
- R-Agent designs rarely enforce execution-level role segregation or permission boundaries.
- All agents can access shared context, call tools, and modify global state indiscriminately.
- There’s no sandboxing, gating, or privilege tiering among sub-agents.
📌 Consequence:
- Risky behavior can’t be isolated;
- Any faulty agent can corrupt the full process;
- Violates basic system design principles (e.g., least privilege, compartmentalization).
4️⃣ Output Is Unverifiable: No Semantic Guardrails
- Sub-agent outputs are often returned as plain natural language without structure.
- There’s no output schema, no verification layer, no status tags, and no embedded context traces.
- Planners have no reliable way to judge whether a task was truly completed or just “looked complete.”
📌 Consequence:
- Fake or hallucinated completions pass silently;
- Planners trust appearances over execution facts;
- Fragile outputs masquerade as intelligent decisions.
🧨 TL;DR:
R-Agent architectures look intelligent—but they still run on sand, not structure.