Stratum FAQs

Question 1

How does Stratum ensure reliable AI agent execution?

Accepted Answer

Stratum ensures reliability by enforcing typed contracts and postconditions against step results, managing workflow state, and providing structured retry logic. If a condition fails, the agent receives specific violations, not just a blind replay, enabling targeted corrections.

Question 2

Can Stratum integrate with existing AI agents or LLMs?

Accepted Answer

Yes, Stratum is designed to give AI coding agents (like Claude Code) a formal execution model via its `stratum-mcp` component. Its `stratum-py` Python library also allows developers to build production LLM systems with various LLM providers (e.g., Groq, Anthropic, OpenAI).

Question 3

What is Stratum and what problem does it solve?

Accepted Answer

Stratum is a state machine dispatch server and Python library that provides a formal execution model for AI agent workflows. It solves the problem of unreliable AI agent execution by managing state, enforcing contracts, and providing structured retries with specific failure context, leading to more robust and auditable LLM systems.

Question 4

What is a 'Gate' in Stratum and how is it used?

Accepted Answer

A 'Gate' in Stratum represents a human-in-the-loop step that pauses workflow execution, awaiting external resolution such as human approval or an agent decision. It enables configurable routing based on outcomes like 'approve', 'revise' (retry a previous step), or 'kill' (terminate the flow).

Question 5

What are the core features of Stratum?

Accepted Answer

Key features include a formal execution model for AI agents, typed LLM orchestration using YAML specifications, automated contract and postcondition enforcement, structured retry logic with failure context, human-in-the-loop (Gate) steps, and comprehensive auditable traces for every action.

Stratum

Stratum

Key Features

Use Cases

Key Features

Use Cases