AI Agent Workflows and Architectures Masterclass

While popular imagination and industry discourse can paint AI agents as complex autonomous systems with a mind of their own, practical implementations are far more straightforward. While specific definitions vary, an AI agent can be as simple as a program making one or more calls to a large language model.

Production systems typically consist of two core components: an LLM-powered router that makes decisions, and a set of functions that execute specific actions. The router’s sophistication can vary— even a simple text summarization system could be considered a basic form of this architecture. Production agent systems typically focus on specific decision points with concrete actions.

What sets LLM-based routers apart from basic classifiers is their ability to handle real-world complexity: they can work across languages, adapt to unexpected inputs, and generalize to new scenarios without additional programming. They are the stochastic, adaptable, next generation of if-else statements.

In a recent AI Agents Masterclass, Jerry Liu (Co-Founder & CEO of LlamaIndex) and Jason Lopatecki (Co-Founder & CEO, Arize), shared their insights on agent workflows and the future of agent-driven architectures. Let’s dive into what they discussed.

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Implementation Architectures for AI Agents

Event-Based Systems

Event-based architectures are one way to build AI agents. Each component in an event-based system operates as an independent unit that responds to and generates events.

Consider a search function within an agent. A user request triggers the router to analyze intent. The router generates an event containing search parameters. A search component picks up this event, executes the search, and generates another event with the results. This chain continues until the interaction completes.

Event-based systems offer natural support for asynchronous processing and parallel execution are particularly helpful when dealing with LLM calls that may have significant latency. Events carry contextual information between steps while maintaining loose coupling, making the system easier to modify and extend over time.

Another advantage—developers can focus on one step at a time without holding the entire system architecture in mind. This supports faster development and easier maintenance compared to more monolithic approaches.

Graph-Based Systems

Graph-based architectures take a different approach by representing agent behavior as interconnected nodes and edges. This creates an explicit state machine where nodes represent specific states or actions, and edges define allowed transitions between them. Developers must define not just what each component does but also the precise conditions under which transitions occur.

This approach works well when mapping complex decision trees or when visual representation of system behavior is required. However, it often requires more upfront planning and can make dynamic behavior changes more challenging compared to event-based systems.

State Management

State management is a challenge in both architectural approaches.

Local state handles information flow between steps or actions. This includes intermediate results, parsed parameters, or temporary context needed for the current interaction. Local state typically lives within the scope of a single transaction or workflow execution.

Global state persists information across multiple interactions or sessions. This becomes particularly important when dealing with context windows, user preferences, or accumulated knowledge that needs to survive across multiple interactions.

For example, AI search functionality often consumes entire context windows, and results can’t be stored in standard message buses as they would exceed context limits in subsequent interactions. Separate state storage solutions and careful management of context window usage are needed.

A pragmatic approach to state management starts simple and adds complexity only when specific use cases demand it.

Production Engineering for AI Agents

Observability and Debugging

AI agents in production need tracing systems that track decision-making across components and interactions. Effective debugging strategies often involve “unfurling” – breaking down loops and exposing the internals of each iteration, especially at points where the system involves user interaction.

Rather than traditional breakpoint debugging, AI agent systems need more comprehensive replay capabilities. Capturing and replaying agent interactions gives developers access into decision-making processes and helps identify areas for improvement. This approach supports both real-time debugging and retrospective analysis of agent behavior.

Each step of the process, from initial input processing through routing decisions, parameter extraction, tool execution, and final output generation, needs visibility to effectively debug and optimize.

Performance Optimization

There are several areas to consider in agent performance optimization.

Routing accuracy is an ongoing challenge. While intent recognition often achieves high accuracy rates, exceeding 90%, parameter extraction tends to be more problematic. Continuous improvement comes from identifying edge cases and enhancing router instructions based on observed patterns.

Token usage varies significantly across frameworks and implementation approaches. More conversational frameworks might use higher token counts due to extensive back-and-forth interactions which can lead to improved results at the cost of increased resource usage. Lower-level frameworks providing direct control over prompts and memory usage allow for more efficient token use.

Optimization strategies should focus on:

• Intent recognition accuracy
• Parameter extraction reliability
• Token usage efficiency
• Response latency
• Resource utilization
• Error handling and recovery

Building for the Future of AI Agents

The AI agent landscape is maturing and teams are prioritizing focused, reliable components over general autonomy. This trend is the result of lessons from production deployments: practical reliability and clear boundaries consistently outperform open-ended autonomous systems.

Production AI agents should start simple, often with a basic router and a few well-defined actions, then expand based on real usage patterns. Tools for state management, debugging, and observability will continue improving, but the core architecture patterns explored here provide a solid foundation.

Success in agent development follows familiar patterns – understand your requirements, choose appropriate architectures, and build incrementally. While the tools are new, the engineering principles remain the same.

Watch the full AI agent masterclass here, check out the presentation slides, and stay tuned for more.