prose

AI agent orchestration uses structured programs to define agent roles, manage parallel execution, handle errors, and pass data context between sessions. This replaces manual coordination with declarative control flow for complex workflows.

State management for parallel AI agent sessions is handled through multiple backends including file-system, in-context, SQLite, and PostgreSQL. These backends ensure robust and scalable session computing when executing concurrent agent workflows.

Yes, PostgreSQL is supported as a state management backend for scalable AI session computing. It allows robust data context passing and session spawning across multiple agents, alongside file-system, in-context, and SQLite options.

Automating a code review process involves defining multiple AI agents where one identifies bugs, another checks style compliance, and a third synthesizes feedback. Structured programs coordinate these roles sequentially or in parallel.

Yes, structured programs for LLM programming support conditional logic, loops, and error handling. This allows complex agent workflows to branch based on outputs and recover from errors during execution.

Structured programs are needed for AI orchestration when workflows require multiple agents, parallel execution, session spawning, or complex data context passing. Direct LLM calls lack built-in control flow for managing these dependencies.