Agentic Reasoning Design Patterns in AI: Examples

In recent years, artificial intelligence (AI) has evolved to include more sophisticated and capable agents, such as virtual assistants, autonomous robots, and conversational large language models (LLMs) agents. These agents can think, act, and collaborate to achieve complex goals. Agentic Reasoning Design Patterns help explain how these agents work by outlining the essential strategies that AI agents use for reasoning, decision-making, and interacting with their environment.

What is an AI Agent?

An AI agent, particularly in the context of LLM agents, is an autonomous software entity capable of perceiving its environment, making decisions, and taking actions to achieve specific goals. LLMs enable these agents to understand natural language and reason through problems. They can also interact with various tools and other agents to solve complex challenges effectively. For instance, a customer support AI agent might use LLMs to understand a user’s query, search a knowledge base for the appropriate solution, and generate a helpful response, adapting its approach based on user feedback to improve future interactions.

Key Design Patterns in AI Agentic Reasoning

Here, we explore four key design patterns: Reflection, Tool Use, Planning, and Multi-agent Collaboration.

1. Reflection

Reflection is the ability of an LLM-based agent to improve its own reasoning through self-assessment and iterative refinement. This approach is particularly powerful for enhancing decision-making accuracy in scenarios like customer support or diagnostics. For example, an LLM-based agent may analyze its previous responses to customer inquiries, identify areas for improvement, and adjust its answers in subsequent interactions, refining its performance over time. Two prominent techniques that embody this pattern are:

• Self-Refine: Iterative Refinement with Self-Feedback
  Madaan et al. (2023) describe how an agent can iteratively refine its responses using its own feedback to improve the quality of its reasoning and decisions. For example, the agent may solve a problem, evaluate its performance, and adjust its approach until the desired outcome is achieved.
• Reflexion: Language Agents with Verbal Reinforcement Learning
  Shinn et al. (2023) introduced a technique where LLM agents use reinforcement learning to verbally reinforce positive behaviors and correct mistakes. This allows agents to learn from their successes and errors in a conversational manner, similar to how a human tutor might offer feedback.

2. Tool Use

LLM agents are not limited to their internal reasoning capabilities; they can also leverage external tools to expand their functionality. Tool use is crucial for extending the agent’s abilities beyond what it can achieve independently. By accessing specialized knowledge, performing tasks that require external data, and interacting with various tools, agents significantly enhance their problem-solving capabilities. For instance, LLM agents can use tools to retrieve up-to-date information from the web, perform calculations, translate languages, or interact with specialized databases. Examples of tool use include:

• Gorilla: Large Language Model Connected with Massive APIs
  Patil et al. (2023) proposed a model that connects to numerous APIs, enabling it to perform tasks such as retrieving data or conducting complex operations. This design turns a language model into an interface for a vast array of services.
• MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action
  Yang et al. (2023) created a system where LLM agents perform multimodal reasoning based on diverse input types, such as images, text, and other data. This versatility makes the agent more capable of handling real-world applications.

3. Planning

Effective LLM agents can create and execute plans by following a sequence of logical steps. Planning is essential for solving complex tasks that require long-term thinking, strategizing, and organizing. Two important approaches to planning include:

• Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
  Wei et al. (2022) demonstrated that prompting large language models to think through problems step-by-step improves their problem-solving abilities. Breaking tasks into logical sequences allows agents to follow chains of reasoning that lead to more accurate results. For instance, in complex troubleshooting, breaking down the process into individual diagnostic steps ensures no critical detail is overlooked, ultimately leading to a more effective solution.
• HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face
  Shen et al. (2023) showed how LLM agents could collaborate with models on Hugging Face to divide and conquer tasks. HuggingGPT orchestrates actions across multiple AI models, ensuring each model contributes to a well-coordinated plan to complete complex tasks.

4. Multi-Agent Collaboration

Collaboration between multiple LLM agents often results in more efficient and sophisticated outcomes, such as improved problem-solving speed, accuracy, and the ability to tackle more complex tasks by pooling resources and expertise. For example, in a medical diagnosis scenario, multiple LLM agents can work together to analyze patient data, cross-reference medical literature, and suggest potential diagnoses, leading to faster and more accurate medical assessments. This pattern relies on agents communicating and solving tasks as a team. Two significant developments in this area include:

• ChatDev – Communicative Agents for Software Development
  Qian et al. (2023) demonstrated how agents could collaborate in software development, assign tasks, and work together efficiently in a pipeline. These agents communicate decisions and progress with one another to ensure smooth workflows.
• AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
  Wu et al. (2023) showcased an innovative way to leverage LLMs for multi-agent conversations. In this setup, agents engage in dialogues to arrive at solutions collectively, reflecting the potential of AI agents working as a coordinated team.

Conclusion

Agentic reasoning patterns provide insights into how LLM agents solve increasingly complex problems. By reflecting on their actions, using tools, planning effectively, and collaborating with others, AI systems can tackle challenges that demand more than just raw computational power. As research advances in areas like Self-Refine, Gorilla, and HuggingGPT, we can expect LLM agents to become more autonomous and capable of managing diverse tasks in real-world environments.

These design patterns represent a step toward a future where LLM agents are not only able to understand and respond to the world but are also continuously improving and expanding their capabilities through sophisticated reasoning techniques.