Linear prompt configurations fail because they cannot loop or make mid-flight corrections. If an LLM returns a malformed schema, a simple prompt sequence has no pathway to recover.
True agency requires modeling pipelines as State Machines. Every system decision maps to a state, and tool outputs govern transition branches. This enables agents to query databases, notice anomalies, retry inputs, and report outcomes dynamically.
We design state-machine agents with strict schemas. We separate cognitive planning logic from task tool runs, resulting in highly reliable backends that execute tasks deterministically.