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Three AI Design Patterns of Autonomous Agents

https://alexsniffin.medium.com/three-ai-design-patterns-of-autonomous-agents-8372b9402f7c

Overview: Three AI Design Patterns of Autonomous Agents​

Autonomous agents are AI systems capable of operating independently to achieve specific goals. There are three foundational design patterns commonly used when building such agents:

1. Reflex Agent Pattern​

  • Description: The simplest form of agent. It reacts to the current situation based on a set of rules or conditions, without considering history or future consequences.
  • How it works: Given an input (observation), the agent immediately produces an output (action) using predefined rules.
  • Example: A thermostat that turns on the heater if the temperature drops below a threshold.

2. Planning Agent Pattern​

  • Description: This agent type plans a sequence of actions to achieve a goal, considering the current state and possible future states.
  • How it works: The agent uses a model of the world to simulate and evaluate different action sequences, choosing the one that best achieves its objective.
  • Example: A chess AI that looks ahead several moves to decide the best move.

3. Learning Agent Pattern​

  • Description: These agents improve their performance over time by learning from experience.
  • How it works: The agent uses feedback from its actions (rewards, punishments, or outcomes) to update its internal model or policy, becoming better at achieving its goals.
  • Example: A recommendation system that gets better as it learns user preferences.

Why are these patterns important?​

  • They provide a framework for structuring AI systems, from simple rule-based bots to complex, adaptive agents.
  • They help in choosing the right approach for a given problem: sometimes a simple reflex agent is enough, while other times you need planning or learning capabilities.

In the context of LLMs and modern AI:​

  • LLMs can be used as components in these patterns, e.g., as the reasoning engine in a planning agent, or as the learning component in a learning agent.
  • Many autonomous AI applications (like AI assistants, game bots, or workflow automation tools) use a combination of these patterns.