How to Build Effective AI Agents (without the hype)

Key Concepts

AI Agents, AI Systems, Workflows, Agents (Entropic's definition), Augmented LLM, Retrieval Augmented Generation (RAG), Tools, Memory, Prompt Chaining, Routing, Paralelization, Orchestrator Worker, Evaluator Optimizer, Deterministic Workflows, Guardrails, Testing and Evaluation.

What are AI Agents and AI Systems?

The video addresses the current hype surrounding AI agents and contrasts it with the challenges even large companies face in implementing effective AI features. It argues that many online examples of AI agents are merely demos and often break down in real-world applications. The core message is that building reliable AI agents is difficult, and the video aims to provide practical tips for developers.

• AI Agents vs. AI Systems: The video emphasizes a distinction between AI agents and AI systems, drawing on a definition from Anthropic.
  • Workflows: Systems where LLMs and tools are orchestrated through predefined code paths. This is what many tutorials online present as AI agents.
  • Agents: Systems where LLMs dynamically direct their own processes and tool usage, maintaining control over how they accomplish tasks.
• Hype vs. Reality: The video acknowledges the hype around AI agents but clarifies that the underlying goal is often simply automation.
• Entropic's Perspective: The video highlights Entropic's recommendation to "find the simplest solution possible" and only increase complexity when needed, suggesting that agentic systems are not always necessary.

Building Effective AI Systems: Core Patterns

The video outlines several patterns for building AI systems, emphasizing that the choice of tool (Python, TypeScript, JavaScript, Make.com, n8n, Flowise) is less important than the underlying patterns used to control the application's flow.

1. Augmented LLM

This is the basic building block, starting with a simple LLM API call and enhancing it through three key elements:

• Retrieval: Pulling information from external sources (databases, vector databases) and making it available to the LLM. This is often implemented using Retrieval Augmented Generation (RAG) with a vector database for similarity search. RAG is likened to giving the LLM long-term memory.
• Tools: Integrating external services or APIs to provide the LLM with access to real-world information (e.g., weather data, shipping updates).
• Memory: Maintaining a record of past interactions with the LLM, allowing it to retain context and build upon previous conversations.

2. Prompt Chaining

Chaining together multiple calls to an LLM, using the output of one call as input for the next. This allows for breaking down complex problems into smaller, more manageable steps.

• Example: Instead of asking the AI to "write a blog post," break it down into steps like: research, topic selection, outline creation, chapter writing.
• Benefits: Increased control at each step due to controlled data and prompts.

3. Routing

Using the LLM to decide which path to take based on the input data and context. This is useful when dealing with multiple scenarios or solutions.

• Process: The LLM categorizes the incoming request, and the application uses routers (if statements or cases) to direct the flow based on the LLM's output.
• Example: Categorizing customer support requests (e.g., "Is it A or B?") and directing them to different workflows accordingly.

4. Paralelization

Making multiple LLM calls in parallel, rather than sequentially. This is ideal for tasks that can be split up and are independent of each other.

• Benefits: Speeds up the application by performing tasks asynchronously.
• Example: Implementing guardrails by having separate prompts evaluate accuracy, harmfulness, and prompt injections in parallel.

5. Orchestrator Worker

A workflow pattern that is more agentic, requiring less explicit programming of steps but still being sequential and linear.

• Process: An LLM assesses the context and determines the steps required to solve a problem.
• Example: In customer care, the LLM analyzes the customer's question, CRM data, and customer care guidelines to determine the necessary actions (e.g., looking up information in the customer care playbook, checking order status, calling the shipping API).

6. Evaluator Optimizer

Using one LLM to create an output and another LLM to review it and provide feedback, which is then used to improve the output.

• Process: Write a blog post -> Critically review the blog post -> Provide feedback -> Use feedback to improve the blog post.

Agent Pattern

The video describes a true agent pattern as a loop where an LLM takes an action, assesses the output within a certain environment, and provides feedback back to itself until it reaches a certain criteria.

• Key Characteristics:
  • Almost no hardcoded steps.
  • Operates within a defined environment with specific instructions and tools.
  • Iterates until a goal is reached or a stopping criteria is met.
• Challenges: Getting reliable results from agentic systems is difficult.
• Example: Devin, the AI software engineer, is cited as an example of a true AI agent that is still under development and not yet fully reliable.

Final Tips for Building Reliable AI Systems

The video concludes with several tips for building reliable AI systems:

1. Be Careful with Agent Frameworks: They can be helpful for getting started, but it's important to understand what's happening under the hood. Building core components from scratch can lead to a better understanding and more control.
2. Prioritize Deterministic Workflows: Start simple and isolate the problem, focusing on achieving near-perfect results before scaling.
3. Don't Underestimate Scaling: Moving from a demo to a large-scale application can introduce significant challenges, including hallucinations.
4. Start with Testing and Evaluation: Implement a proper testing and evaluation system from the beginning to ensure that changes improve the application.
5. Put Proper Guardrails in Place: Implement checks to ensure that the output is safe and appropriate before sending it to the customer or application.
6. (Optional) Generative AI Launchpad: The video mentions a paid product called Generative AI Launchpad, which provides access to Data Lumina's code structure, infrastructure, and deployments for building AI systems.

Synthesis/Conclusion

The video provides a practical guide for developers looking to build effective and reliable AI systems. It emphasizes the importance of understanding the distinction between AI agents and AI systems, choosing the right patterns for the task at hand, and prioritizing deterministic workflows over complex agentic systems. The video also highlights the challenges of scaling AI applications and the importance of testing, evaluation, and guardrails. The core message is to start simple, build incrementally, and focus on achieving reliable results before scaling up.