Building LLM Agents: Insights from Anthropic on Latent Space

November 29, 2024 4 minute read

The recent Latent Space podcast with Erik Schluntz, a member of the technical staff at Anthropic, explored agents — programs powered by Large Language Models designed to autonomously perform tasks by reasoning and acting in dynamic environments. While the discussion focused on agents and their application in coding, many of the insights apply broadly to building applications with LLMs. I captured these takeaways as notes for myself and thought they might be useful to share.

Key Concepts

As background, here are some key concepts to be familiar with:

Agent in LLMs: An agent in LLMs is an autonomous system that combines reasoning, planning, and action to accomplish tasks. It uses an LLM for understanding instructions, generating responses, and making decisions. Agents often interact with external tools, APIs, or environments to execute tasks. For example, an agent might query a database, automate workflows, or draft a report. Agents can also break down complex problems into smaller steps, reason through each, and adapt dynamically based on the situation.
Agent Framework: A framework provides structure and tools for building LLM agents. It includes reusable components, such as modules for managing tool interactions, handling context, or structuring workflows, allowing developers to focus on defining agent behaviors instead of starting from scratch.
Context in LLMs: Context refers to the information available to the LLM at any point. LLMs have a limited “context window” for holding text, such as recent interactions, instructions, or data. Efficient context management ensures accurate and coherent responses, especially in longer tasks.
Tools for LLMs: LLMs can interact with external systems or resources, extending their functionality beyond text generation. For example, an LLM might query a database, call an API, or interact with software to perform specific tasks. Developers can also implement custom tools for the LLM to use and define how the tool should work. This involves describing the tool’s purpose, inputs, and expected outputs so the LLM can understand and use it effectively. These capabilities allow LLMs to become dynamic participants in workflows, solving real-world problems more effectively.
Computer Use In Claude Sonnet: Computer use refers to LLMs and agents performing tasks directly within a computer’s graphical interface by simulating human interactions, such as clicking buttons, moving a mouse, or typing. This allows agents to interact with software without dedicated APIs, making it ideal for prototyping or systems lacking APIs.

Key Takeaways

Here are the take-aways I found most interesting from the podcast episode:

Tool Design as UI/UX: Treat tool creation for LLMs not as simply defining an API, but as designing a user-friendly application. Consider the LLM’s “user experience” – provide comprehensive documentation, rich examples within the tool’s description, and carefully consider how the model will represent its intended actions. Critically, observe how the LLM attempts to use the tool in practice, making adjustments for clarity and ease of use. This human-centric approach improves reliability, reduces the cognitive load on the LLM, and minimizes potential misunderstandings. Strive to create “foolproof” tools, just as you would for human users.
Start Simple, Then Use Frameworks: Master prompting and tool building before incorporating agent frameworks. While frameworks can simplify the development of complex systems, a strong foundation in prompting and basic tool usage is essential. This foundational knowledge helps prevent over-engineering by promoting a deeper understanding of LLM capabilities and how LLMs interact with tools. Frameworks can then be leveraged effectively when increased complexity demands it.
Effective Context Management: Managing context is crucial, especially for longer conversations. Employ multiple strategies: pruning irrelevant dialogue turns, summarizing past interactions to retain essential information concisely, and delegating specific sub-tasks to smaller, specialized LLMs (like Anthropic’s Haiku) to free up the main LLM’s context window. For exceptionally long tasks, explore methods to segment and summarize previous portions of the conversation to manage context effectively.
Structured Prompts (XML): Use XML tags to structure prompts, similar to how HTML structures websites. This enhances clarity and parsability, providing a clear way to delineate sections within a prompt (e.g., code examples, distinct instructions). This structured approach is often preferred internally at Anthropic, and leads to more reliable / higher quality model outputs.
Computer Use vs. API Integration: Computer use agents, interacting directly with graphical interfaces, offer a rapid and flexible alternative to building dedicated API integrations. While potentially less robust due to the inherent complexities and dynamic nature of interacting with real-world applications, computer use excels in rapid prototyping and scenarios where development speed is prioritized. The trade-off lies between faster development and the higher reliability offered by direct API integrations.
Tailored Prompts: Avoid generic prompts. Create a library of prompts tailored to the specific complexity and requirements of different tasks. Implement a top-level classification system (either rule-based or driven by another LLM) to dynamically select the appropriate prompt for each incoming request. This ensures the LLM receives the right level of guidance and context, optimizing performance and preventing over-complication for simpler tasks.
Embrace Iterative LLM Autonomy: Allow the LLM room to “think” and iterate. Avoid overly prescriptive workflows, letting the model explore different solution paths, self-correct, and learn from its attempts. This approach, which Erik refers to as “giving Claude the reins,” often leads to more creative and effective outcomes than rigidly defined procedures.

If these ideas resonated, I recommend listening to the episode to get the full details. Latent Space consistently delivers thoughtful discussions on AI and LLMs.