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Agent Model

(This document is a placeholder. It will outline the conceptual model for agents built using Dana, including their core components, lifecycle, and how Dana serves as the language for defining their behavior and reasoning processes.)

Key Aspects to Define:

  • Definition of a Dana Agent:
  • An autonomous entity that perceives its environment (or input data), makes decisions (plans), and takes actions (executes capabilities) to achieve goals.
  • Primarily defined by Dana code and configured resources.
  • Core Components of a Dana Agent (Conceptual):
  • Perception: How an agent ingests information (e.g., from data sources, user input, sensor feeds via Resources).
  • World Model / State: How an agent maintains its understanding of the current situation. This is largely managed by the SandboxContext.
  • Goal Definition: How an agent's objectives are specified (e.g., as Dana structs, initial parameters, or natural language prompts processed by an LLM).
  • Planning/Reasoning Engine:
  • The core logic, often implemented as Dana functions (potentially IPV-enabled like reason()).
  • May involve breaking down complex goals into smaller, manageable tasks.
  • Leverages LLMs for complex reasoning, and Dana for deterministic logic and capability orchestration.
  • Action/Execution Engine:
  • Invokes capabilities via Resources (LLMs, tools, APIs).
  • The Dana interpreter executing Dana code that calls resource methods.
  • Learning/Adaptation (Future): How agents might improve their performance over time (e.g., by updating their internal knowledge, refining plans, or even suggesting modifications to their own Dana code).
  • Agent Lifecycle:
  • Initialization (loading Dana code, configuring resources, setting initial state).
  • Execution Loop (e.g., perceive-plan-act cycle).
  • Termination/Shutdown.
  • Types of Agents:
  • Simple reactive agents.
  • Goal-oriented agents.
  • Potentially more complex BDI (Belief-Desire-Intention) agents if Dana evolves to support such constructs.
  • Role of Dana Language:
  • Defining agent behavior, decision-making logic, and control flow.
  • Orchestrating calls to various capabilities (LLMs, tools, data sources).
  • Managing agent state within the SandboxContext.
  • Example Agent Structure (Conceptual):
  • A main Dana script defining the agent's primary loop or entry point.
  • Dana functions for specific tasks or behaviors.
  • Configuration for required resources.

Self-reflection: The Dana agent model should emphasize flexibility, allowing developers to implement a range of agent architectures from simple to complex, with Dana providing the core symbolic control and reasoning glue.