Design Principles for Scalable Agents
1. Design Principles for Scalable Agents
Welcome back! Now we've refreshed ourselves on AI agents and their use cases, let's talk about what makes agents scalable, and some core design principles for scalable agents.2. Scalable or not scalable?
Put simply, scalable agents are able to withstand the challenges of high user traffic, maintaining consistently high performance. High user traffic brings3. Scalable or not scalable?
greater variety in user inputs, potentially pushing your agent beyond what it has encountered before.4. Scalable or not scalable?
A greater likelihood of malicious actors trying to misuse the agent, and5. Scalable or not scalable?
more model usage and tool calls. To mitigate risks during scaling, we'll propose three core design principles for scaling AI agents:6. The pillars of scalable agentic systems
robust infrastructure and tooling, a modular design architecture, and continuous evaluation and feedback loops.7. Robust infrastructure and tooling
Compute and storage are key infrastructure factors for AI applications. Compute refers to the amount of computational resources available, which in AI agents, will be used for running the agent code and processes in the workflow. Storage is used for storing agent states, which essentially log the processes and outputs while the agent is running, the user chat histories, and any extra logs for monitoring and debugging. Alongside the compute and storage, we need8. Robust infrastructure and tooling
reliable deployment pipelines, so updates and changes can be made to the application with low risk to user experience. We'll see how the modularity pillar can help with this. Finally, using established agent tooling to handle tool calling, multi-step reasoning, and memory management will give your system a strong backbone to build off.9. Modularity
The second pillars is modularity, which is the principle of designing systems as separate, independent components that are combined to create a larger whole. Modularity is a common practice across many forms of development, including learning design and software engineering, and it allows for the design, development, and maintenance of the components to be carried out largely without impact to the other components. Let's take the example of LEGO bricks. There are a finite number of distinct LEGO bricks that can be used to build a larger structure.10. Modularity
If LEGO decided they wanted to change the shade of yellow used in their standard yellow bricks,11. Modularity
they wouldn't need to touch the other colored bricks in their catalogue. So how does this apply to AI agents?12. Modularity in agents: Software modularity
First, applications components like the user interface, the agents themselves, and data stores are designed as separate components with their own logic, in contrast to a single monolithic structure. This allows for easier updates, maintenance, and monitoring, as each component can be worked on in isolation. Modular software design is nothing new to software engineers, but because of the number and diversity of components in agentic systems, it becomes a necessity.13. Modularity in agents: Multi-agents
Second, we can design our agents to be modular components themselves. Rather than giving a single agent lots of tools to handle every aspect of a task, we can use multiple agents.14. Modularity in agents: Multi-agents
Each agent in a multi-agent system operates over a clearly-defined task domain.15. Example: Customer support multi-agent
For example, in a customer support multi-agent system, one agent might be used to retrieve information from different sources, and another might be specialized for responding to the user with a certain tone and style.16. Sneak peak at Chapter 2
We'll look at different multi-agent architectures in Chapter 2.17. Continuous evaluation and feedback loops
The final pillar is continuous evaluation and feedback loops. Every major part of your agent should be observable - meaning you can track what's going on and catch issues early.18. Continuous evaluation and feedback loops
By logging and tracking things like success rates, latencies, and error patterns, you create a foundation for debugging, improving, and scaling reliably.19. Continuous evaluation and feedback loops
Collecting user feedback is also crucial for driving improvements and detecting issues. A common approach is to implement a thumbs-up/thumbs-down mechanism for users to rate agent outputs. This feedback becomes the fuel for improvement - whether that's updating prompts, choosing new tools, providing more data, or just tweaking parameters.20. Let's practice!
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