In the rapidly evolving field of artificial intelligence, foundation models are playing an increasingly central role. These models, often characterized by their ability to understand and generate human-like text, are now being integrated into a wide variety of applications. To optimize the implementation of these foundation models, a comprehensive set of design patterns has been developed. These patterns are aimed at enhancing the efficiency, adaptability, and robustness of AI agents. In this blog post, we'll delve into a catalogue of these design patterns, exploring how each one contributes to the development of sophisticated, goal-oriented agents.
Pattern Catalogue Overview
1. Passive Goal Creator
The passive goal creator pattern involves analyzing users' articulated prompts through a dialogue interface. This approach preserves interactivity, goal-seeking, and efficiency by ensuring that the AI system responds accurately to user inputs without overstepping its bounds.
2. Proactive Goal Creator
In contrast to the passive goal creator, the proactive goal creator anticipates users' goals by understanding human interactions and capturing context via relevant tools. This pattern enhances interactivity, goal-seeking, and accessibility, making the AI more intuitive and user-friendly.
3. Prompt/Response Optimiser
This pattern focuses on optimizing the prompts and responses according to the desired input or output content and format. The goal is to provide standardization, goal alignment, interoperability, and adaptability, ensuring consistent and relevant interactions.
4. Retrieval Augmented Generation
Retrieval augmented generation enhances the knowledge update ability of agents while maintaining data privacy. This pattern is crucial for on-premise foundation model-based agents and systems implementations, ensuring that AI systems can stay current without compromising sensitive information.
5. One-shot Model Querying
With one-shot model querying, the foundation model is accessed in a single instance to generate all necessary steps for a plan. This method is known for its cost efficiency and simplicity, reducing the computational overhead associated with multiple queries.
6. Incremental Model Querying
Unlike one-shot querying, incremental model querying accesses the foundation model at each step of the plan generation process. This approach provides supplementary context and improves reasoning certainty and explainability.
7. Single-path Plan Generator
The single-path plan generator orchestrates the generation of intermediate steps leading to the achievement of the user's goal. It aims to improve reasoning certainty, coherence, and efficiency by following a clear, linear path.
8. Multi-path Plan Generator
This pattern allows multiple choice creation at each intermediate step, leading to achieving users' goals. It enhances reasoning certainty, coherence, and alignment to human preference and inclusiveness by considering various potential pathways.
9. Self-reflection
Self-reflection enables the agent to generate feedback on the plan and reasoning process and provide refinement guidance from themselves. This continuous improvement cycle aims to improve reasoning certainty, explainability, and efficiency.
10. Cross-reflection
In cross-reflection, different agents or foundation models provide feedback and refine the generated plan and reasoning process. This collaborative approach fosters better reasoning certainty, explainability, inclusiveness, and scalability.
11. Human Reflection
Human reflection involves collecting feedback from humans to refine the plan and reasoning process. By aligning effectively with human preference, this pattern improves contestability and effectiveness, ensuring the AI system meets user expectations.
12. Voting-based Cooperation
This pattern enables free opinions expression across agents, allowing them to reach consensus by submitting their votes. It preserves fairness, accountability, and collective intelligence, making decision-making processes more democratic and transparent.
13. Role-based Cooperation
In role-based cooperation, agents are assigned roles, and decisions are finalized in accordance with these roles. This pattern facilitates the division of labor, fault tolerance, scalability, and accountability, ensuring that each agent contributes effectively to the overall task.
14. Debate-based Cooperation
Debate-based cooperation involves providing and receiving feedback across multiple agents, adjusting thoughts and behaviors through debate until a consensus is reached. This pattern aims to improve adaptability, explainability, and critical thinking.
15. Multimodal Guardrails
Multimodal guardrails control the inputs and outputs of foundation models to meet specific requirements such as user needs, ethical standards, and laws. This pattern enhances robustness, safety, standard alignment, and adaptability, ensuring that AI systems operate within acceptable boundaries.
16. Tool/Agent Registry
Maintaining a unified and convenient source to select diverse agents and tools is the focus of the tool/agent registry pattern. It improves discoverability, efficiency, tool appropriateness, and scalability, making it easier to find and implement the right tools for specific tasks.
17. Agent Evaluator
The agent evaluator pattern involves performing testing to assess the agent regarding diverse requirements and metrics. It ensures functional suitability, adaptability, and improved flexibility, providing a comprehensive evaluation framework for AI systems.
Conclusion
Foundation model-based agents are revolutionizing the way we interact with AI systems, and these design patterns are essential for maximizing their potential. By incorporating these patterns, developers can create more interactive, efficient, and adaptable AI agents that align closely with human preferences and needs. As AI continues to advance, these patterns will play a crucial role in shaping the future of intelligent systems.
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