LLM-based Agent System Component

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An LLM-based Agent System Component is an LLM-based system component within an LLM-based agent architecture.

  • Context:
    • ...
  • Example(s):
    • LLM-based User Interface Component: Provides interfaces for user interaction including text, voice, GUI elements etc. Key capabilities include input parsing and validation, response rendering in different modalities, interfacing with output generation modules. Should support standardized interfaces for connecting to agent modules.
    • LLM-based User-Input Processing Component: Responsible for analyzing user input. Key capabilities include linguistic analysis, understanding nuances, extracting entities and intents. Should provide standardized interfaces to return extracted information.
    • LLM-based Knowledge Base Component: Provides standardized access to external structured knowledge sources like databases, knowledge graphs, ontologies etc. Capabilities include knowledge ingestion, organization, storage and retrieval. Should support interfaces for diverse knowledge sources.
    • LLM-based Memory Component: Maintains history of conversations, interactions, relationships, entities etc. Provides continuity and personalization. Capabilities include ingesting data, organizing, storage and retrieval. Needs interfaces for short-term and longer-term memory.
    • LLM-based Retrieval Component: Retrieves relevant knowledge from memory and knowledge bases based on context and user input. Key capabilities include semantic search over interaction memory and factual knowledge. Needs interfaces to query connected memory/knowledge components.
    • LLM-based LLM-Engine Component: Generates conversational responses based on context, retrieved knowledge etc. Key capabilities include language generation and disambiguation. Needs interfaces to receive context data and return responses.
    • LLM-based Response Selection Component: Selects best response from candidates based on criteria like coherence. Key capabilities include candidate generation, scoring and selection. Requires interfaces to receive candidates and return selections.
    • LLM-based Planning Component: Strategizes goals and formulates plans by breaking down goals into executable steps. Key capabilities include goal formulation, hierarchical planning, plan optimization. Needs interfaces to set goals and retrieve plans.
    • LLM-based Action Component: Executes planned actions and steps by querying APIs/services. Capabilities include resolving plans into invocations and interactions. Requires interfaces to receive plans and execute steps.
    • LLM-based Agent Profile Component: Characterizes key agent attributes like persona, capabilities etc. Capabilities include representing attributes in a machine-readable format. Needs interfaces to access and update profiles.
    • LLM-based Evaluation Component: Assesses performance based on metrics like accuracy, coherence etc. Capabilities include metric definition, instrumentation of components, analysis and reporting. Requires interfaces to retrieve system data.
    • LLM-based Mitigation Component: Implements strategies to address challenges around robustness, security etc. Capabilities include detecting anomalies and applying mitigations. Needs interfaces to access system signals and apply fixes.
    • LLM-based Monitoring Component: Tracks metrics like latency, usage etc. for system management. Capabilities involve monitoring infrastructure and components. Requires interfaces to capture metrics.
    • LLM-based Orchestration Component: Coordinates components and handles workflows. Capabilities include managing states, transitions, concurrency. Needs interfaces to invoke components.
    • ...
  • Counter-Example(s):
  • See: LLM-based QA System, LLM-based Chatbot.


References

2023

2023