Skip to content

Latest commit

 

History

History
49 lines (30 loc) · 2.58 KB

ai-agents-overview.md

File metadata and controls

49 lines (30 loc) · 2.58 KB

AI Agents

Language model powered AI agents and state machines have emerged as a promising design pattern for creating flexible and effective ai-powered products. Agents use LLMs as general-purpose problem-solvers, connecting them with external resources to answer questions or accomplish tasks.

The structure and design pattern of an LLM program is commonly called cognitive architecture.

Agent Architecture

The key components in AI Agentic framework are:

  • Planning

    • Subgoal and decomposition: The agent breaks down large tasks into smaller, manageable subgoals, enabling efficient handling of complex tasks.
    • Reflection and refinement: The agent can do self-criticism and self-reflection over past actions, learn from mistakes and refine them for future steps, thereby improving the quality of final results.

  • Memory

    • Short-term memory: I would consider all the in-context learning (See Prompt Engineering) as utilizing short-term memory of the model to learn.
    • Long-term memory: This provides the agent with the capability to retain and recall (infinite) information over extended periods, often by leveraging an external vector store and fast retrieval.

  • Tool use

    • The agent learns to call external APIs for extra information that is missing from the model weights (often hard to change after pre-training), including current information, code execution capability, access to proprietary information sources and more.

ReAct Agent

The ReAct agent is a great prototypical design for this, as it prompts the language model using a repeated thought, act, observation loop:

Thought: I should call Search() to see the current score of the game.
Act: Search("What is the current score of game X?")
Observation: The current score is 24-21
... (repeat N times)

This takes advantage of Chain-of-thought prompting to make a single action choice per step.

Plan-And-Execute

Plan-and-Solve Prompting is a simple planning agent architecture consists of two basic components:

  • A planner, which prompts an LLM to generate a multi-step plan to complete a large task.
  • Executor(s), which accept the user query and a step in the plan and invoke 1 or more tools to complete that task.

Once execution is completed, the agent is called again with a re-planning prompt, letting it decide whether to finish with a response or whether to generate a follow-up plan.

References