"LLM models are useless without access to data. They can generate text, but to find suitable candidates for open vacancies, they must call real APIs, read results from a database, and make decisions based on the current state of the system."
"We have built an autonomous agent that, based on a user's prompt, searches a multi-tenant database, filters results, and orchestrates sub-tasks between services written in different languages."
"A fundamental problem in building agents is standardizing communication between the AI model and external systems. Instead of building a dedicated integration for each model separately, we relied on the Model Context Protocol (MCP)."
"TypeScript serves here solely as a thin adapter translating the MCP protocol into HTTP calls to our Pekko backend. All 'hard' business logic remains within the core domain logic."
LLM models depend on data access to generate meaningful outputs. They must interact with APIs and databases to make informed decisions. An autonomous agent has been developed to search databases and manage tasks across different programming languages. This system utilizes the Model Context Protocol for standardized communication between AI models and external systems. The architecture combines JVM and Rust ecosystems, ensuring seamless integration and scalability while maintaining high performance through Apache Pekko HTTP.
Read at Scalac - Software Development Company - Akka, Kafka, Spark, ZIO
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