Short answer: CrewAI's hierarchical process assigns tasks through a manager agent's live LLM judgment call, made fresh on every run. Anter validates every transfer and delegation against a write-time allowlist and a runtime existence check before it's allowed to happen, and persists the outcome as a durable event.
What "manager agent" routing means in practice
Per CrewAI's own docs on Processes, the hierarchical process pattern puts a manager agent in charge of "planning, delegation, and validation." That's a real capability — but the routing decision itself is a model inference, made fresh each run. Nothing outside the manager's own prompt constrains where a task can go; if the manager's judgment drifts (a longer conversation, an ambiguous task description, a model swap), the routing can drift with it, silently.
Two checks, not zero
Anter's routing graph is defined once, at build time, as an explicit allowlist of which agents can transfer or delegate to which. At runtime, every attempted handoff is checked twice:
- Write-time allowlist — is this edge one that was actually authored into the graph?
- Runtime existence check — does the target agent still exist and is it still reachable in the current execution context?
Only after both checks pass does the handoff execute — and it's persisted as a structured routing event atomically, before the next step runs, so there's a durable record of exactly what happened and why, reconstructable after the fact without a rerun.
Bounded, not just validated
Multi-agent systems that hand routing entirely to live LLM judgment have no inherent limit on how deep or how expensive a chain of hand-offs can get. Anter pairs validated routing with a cycle guard, hop-depth ceiling, and step-budget decay, so a validated graph is also a bounded one.
See the full side-by-side comparison for how this compares to Claude Managed Agents, OpenAI AgentKit, and Base44 as well.