AI’s Learning Journey: A Parallel to Humans
Learning is an ongoing adventure, whether for humans or AI models. A common query is whether AI models can learn autonomously like us. Recent progress indicates they can. Recall college days when mastering languages like C++, Java, and Python was crucial for computer – science success. Grasping these languages meant understanding syntax, semantics, application, and problem – solving. We practiced and trained continuously and learned from peers and professors. Similarly, just as humans learn from multiple sources, large language models (LLMs) may also have the capacity to learn from various mediums.
The Rigorous Path to Expertise for Humans and LLMs
Becoming an expert is a tough journey for both humans and LLMs. We’re familiar with the human learning process, but what about LLM training? It involves pre – training, where the model learns patterns like grammar and sentence structure; instruction – tuning, using a curated dataset for fine – tuning; and Reinforcement Learning with Human Feedback (RLHF), where human evaluators rank responses to better align the model with user expectations.
Agentic Workflows: The Future of AI Autonomy
What if we create an agentic workflow that allows the model to learn and output independently? It would be like having a self – sufficient assistant. In this article, we’ll explore four Agentic AI Design Patterns for architecting AI systems.
Understanding Agentic Design Patterns
The agentic design pattern aims to make LLMs more autonomous. Instead of a single prompt for a final answer, an agent – like approach prompts the LLM step – by – step, refining the task and output iteratively. For example, when writing code with an agentic workflow, we plan an outline, gather information, write a first draft, review for errors, and revise until the code is clean and efficient.
Evaluating Agentic Design Patterns
Andrew Ng’s analysis on Deeplearning.ai focused on AI – driven code generation, particularly GPT – 3.5 and GPT – 4. On the HumanEval coding benchmark, GPT – 3.5 achieved 48.1% correctness in zero – shot mode, while GPT – 4 had a 67.0% success rate. However, when integrated into an iterative agent workflow, GPT – 3.5’s accuracy soared to 95.1%, highlighting the potential of such workflows.
Four Key Agentic Design Patterns
Reflection Pattern: This pattern improves AI’s self – evaluation and refinement of outputs. For example, in software writing, the LLM can critique and revise its own code iteratively. An interesting example is Self – Reflective RAG, which enhances language model quality through self – reflection and adaptive retrieval.
Tool Use Pattern: It broadens an LLM’s capabilities by enabling interaction with external tools, like accessing databases or executing Python functions, making it more versatile for complex tasks.
Planning Pattern: Allows an LLM to break down large tasks into smaller components. Approaches like ReAct and ReWOO integrate decision – making and contextual reasoning for more adaptive planning.
Multi – Agent Pattern: Similar to project management in human teams, different agents handle subtasks, collaborating to achieve a unified result. There are collaborative, supervised, and hierarchical multi – agent types.
Conclusion
Agentic Design Patterns have the potential to transform AI models, making them more autonomous and efficient. By mastering the four key patterns—Reflection, Tool Use, Planning, and Multi – agent—we can unlock the full potential of AI systems, enabling them to handle real – world challenges more like humans. Future AI advancements will likely depend on developing more adaptive workflows rather than just increasing model size.
