As the platform scaled, the company faced challenges with performance and scalability, leading to architectural changes such as organization-based sharding and the extraction of components into separate services. They adopted CQRS with Event Sourcing for agent memory management and explored solutions for handling natural language events. The company also addressed scaling issues by optimizing their architecture and moving to Azure's GPT deployments. Despite the complexities of distributing agents, they continued to model them as objects, using Data Transfer Objects for APIs. They explored existing solutions like Temporal for orchestrating long-running, stateful workflows, aiming to enhance the durability and resilience of their system.
Key takeaways:
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- The company initially launched an AI-powered Chief of Staff for engineering leaders, which gained significant traction and led to the development of Outropy, a platform for building AI products.
- The transition from a monolithic architecture to a distributed system involved challenges, particularly with scaling AI agents and inference pipelines, which required innovative solutions like CQRS and event sourcing.
- Agents in the system were modeled using object-oriented programming principles, which provided a more natural abstraction compared to microservices, especially given the stateful and non-deterministic nature of AI agents.
- Scaling challenges were addressed through sharding, asynchronous processing, and eventually moving to a distributed architecture with services like GPTProxy, while maintaining performance and reliability.