This case study details the journey of Outropy in building and scaling an AI-powered Chief of Staff system that evolved into a developer platform. The narrative provides valuable insights into the practical challenges and solutions in deploying LLMs in production at scale. The company started with a focused approach, building a Slack bot that used simple inference pipelines. As they grew to handle 10,000 users, they had to solve several significant LLMOps challenges, particularly around architecture, scaling, and reliability. Their journey represents a practical example of evolving from a prototype to a production-grade AI system. The core architecture underwent several significant transitions: Initially, they implemented a traditional microservices approach but discovered that AI agents' characteristics created fundamental mismatches with microservice principles. The statefulness of agents, their non-deterministic behavior, and their data-intensive nature with poor locality made traditional microservice patterns ineffective. This led them to pivot to an object-oriented approach for agent implementation, treating agents as stateful entities with distinct identities and lifecycles. A key innovation in their system was the handling of agent memory and state. They implemented a hybrid approach: * Simple agents used straightforward SQLAlchemy ORM storage * Complex agents utilized CQRS (Command Query Responsibility Segregation) with Event Sourcing * They developed a semantic event bus system allowing agents to subscribe to relevant events without creating tight coupling For natural language processing, they developed a sophisticated event handling system using proposition-based retrieval, converting unstructured messages into structured propositions that could be efficiently processed by their agents. This approach helped manage the complexity of processing natural language inputs while maintaining system reliability. As they scaled to handle thousands of users, they encountered and solved several critical LLMOps challenges: * Performance degradation in personal daily briefings was addressed through organization-based sharding * OpenAI rate limits were managed through a token budgeting system and migration to Azure's GPT deployments * They implemented sophisticated backpressure mechanisms and resilience patterns * The system was eventually migrated to use Temporal for workflow management Their scaling solution evolved through several stages: 1. Initial monolithic deployment on AWS ECS 2. Implementation of organization-based sharding 3. Optimization of async processing using Python's features 4. Migration to Azure's GPT deployments for better quota management 5. Extraction of the GPT proxy into a dedicated service 6. Development of a distributed agent architecture The case study also highlights important lessons about LLM-based system architecture: * The importance of separating inference pipelines from agent logic * The need for robust event processing systems when dealing with natural language * The value of maintaining simplicity in early stages while building toward scalability * The challenges of managing LLM API costs and rate limits at scale Their handling of LLM integration shows sophisticated understanding of production requirements: * Implementation of fallback mechanisms * Caching strategies * Exponential backoff for API calls * Load balancing across multiple GPT deployments * Token budget management systems The case study concludes with their adoption of Temporal for workflow management, which proved crucial for handling long-running, stateful operations. This choice helped solve many challenges around durability and resilience, though they had to build additional tooling around the Python SDK. A particularly interesting aspect of their journey was the evolution of their event processing system. Their solution to handling natural language events through proposition-based retrieval demonstrates a practical approach to making LLM-based systems more reliable and efficient in production environments. The company's experience offers valuable insights for others building production AI systems, particularly in areas of: * Architecture evolution * Scaling strategies * State management * Event processing * Error handling * Resource optimization * Platform selection Their journey from a simple bot to a sophisticated multi-agent system illustrates the real-world challenges of building production-grade AI applications and provides practical solutions for common LLMOps challenges.