Drift has been introduced—a new domain-specific language (DSL) that allows describing the operational logic of LLM agents in natural English, followed by automatic translation into optimized asynchronous Python code.
What happened
A developer has introduced Drift, a tool implementing an intent-based approach to creating agentic systems. The language supports parallel step execution, budget management, multi-provider model routing, and native integration with MCP (Model Context Protocol) standard tools.
Context
Modern development of complex agentic chains requires a deep understanding of asynchronous Python programming to ensure performance and reliability. Drift offers an abstraction layer that shifts the focus from writing low-level code to declarative intent design.
Why it matters for the industry
The tool could significantly accelerate the R&D cycle for startups and simplify the integration of agentic abstractions into existing development pipelines. In the long term, such DSLs could become the de facto standard for describing the logic of complex AI systems, much like SQL became the standard for data manipulation.
Why it matters for users
Developers gain the ability to quickly prototype complex agent functions and create MVPs using natural language instead of writing cumbersome asynchronous code, while retaining all the power, performance, and type safety of Python.
What is not yet known / limitations
There are risks associated with the reliability of the translation process from English to Python, as well as questions regarding the auditability of the logic accepted within an automated system.
Sources
Author
Look at AI, Editorial Team