Building Effective AI Agents

Summary

Summarized wtih ChatGPT

The text discusses building effective AI agents using large language models (LLMs), emphasizing the difference between workflows and agents. It provides practical advice for developers, suggesting to start with simple solutions and only add complexity when necessary. The article highlights the importance of testing and transparency in agent design for optimal performance.

Key Takeaways:

1. Begin with simple LLM implementations before adding complexity.
2. Focus on transparency and clear planning steps in agent design.
3. Test agents extensively in controlled environments to ensure reliability.

Highlights from Article

Agents, on the other hand, are systems where LLMs dynamically direct their own processes and tool usage, maintaining control over how they accomplish tasks.

Agentic systems often trade latency and cost for better task performance, and you should consider when this tradeoff makes sense.

Prompt chaining decomposes a task into a sequence of steps, where each LLM call processes the output of the previous one.

When to use this workflow: Parallelization is effective when the divided subtasks can be parallelized for speed, or when multiple perspectives or attempts are needed for higher confidence results.

Workflow: Evaluator-optimizerIn the evaluator-optimizer workflow, one LLM call generates a response while another provides evaluation and feedback in a loop.

• Background: I need a comprehensive list of all workflows discussed in “Building Effective AI Agents” and guidelines on when to use each workflow.

🌟 Evaluator-optimizer Workflow: This workflow is useful when tasks can be parallelized for speed or when multiple attempts are needed to achieve higher confidence results. It involves one LLM generating responses while another evaluates and provides feedback in a continuous loop.

⚙️ Prompt Chaining: Use this workflow when you need to break down a complex task into a sequence of manageable steps. Each LLM call processes the output of the previous one, allowing for a structured approach to problem-solving.

🔄 Agentic Systems: Consider implementing agentic systems when you need LLMs to dynamically control their processes and use tools. This approach can enhance task performance, but be mindful of the trade-offs in latency and cost.

In this section, we’ll explore the common patterns for agentic systems we’ve seen in production. We’ll start with our foundational building block—the augmented LLM—and progressively increase complexity, from simple compositional workflows to autonomous agents.

• Types of workflows:
• Basic where a query goes to an LLM (with tools) and then on
• Prompt chaining, with multiple steps
• routing - where one query is routed to a specific LLM by another LLM
• Parallelization - send to multiple ones at once
• Orchaestration - send multiple at once for differnt tasks
• Evaluate/optimize feedback to make sure it’s okay Agents

gents can handle sophisticated tasks, but their implementation is often straightforward. They are typically just LLMs using tools based on environmental feedback in a loop.

All material owns to the authors, of course. If I’m highlighting or writing notes on this, I mostly likely recommend reading the original article, of course.

See other recent things I’ve read here.