Unverified50% confidenceFactExact time
RadOnc-Agent's LLM controller is responsible for three tasks: intent mapping, context maintenance, and request routing.
1
Sources
50%
Confidence
Long-term
Relevance
10/7/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedRadOnc-Agent's LLM is positioned as an orchestrator rather than an executor, with specialized computations delegated to domain-specific modules.74% similarUnverifiedCompared to traditional RPA, LLM-driven Agents can handle unstructured inputs and dynamically adjust execution paths rather than strictly following predefined rules.68% similarUnverifiedIn the MAS paradigm of the LLM era, each Agent uses an LLM as its core reasoning engine, and is capable of dynamically planning action sequences, invoking external tools, and negotiating with other Agents.67% similarUnverifiedEnterprise-grade Agents must possess three core capabilities: knowledge retrieval (RAG), tool invocation (Skill/Function Calling), and task orchestration (multi-agent collaboration).67% similarUnverifiedRadOnc-Agent's real patient case workflows cover two typical processes: decision-to-planning and planning-to-adaptive.66% similar
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