Software 3.1? – AI Functions
6 hours ago
- #LLM
- #AI-Functions
- #Software-3.1
- AI Functions represent Software 3.1, moving beyond Software 3.0's generate-and-verify loop by executing LLM-generated code at runtime and using automated post-conditions for continuous verification.
- Software 1.0 is human-written code, Software 2.0 involves neural network weights learned through optimization, and Software 3.0 is prompting LLMs in plain language.
- AI Functions allow LLMs to generate and execute code within applications, returning native Python objects like DataFrames and Pydantic models instead of serialized text.
- Post-conditions in AI Functions validate outputs on every call, feeding failures back to the model for automatic retries, shifting developer focus from perfect prompts to good post-conditions.
- AI Functions can return non-serializable Python objects, enabling dynamic data handling and format-agnostic operations without manual parsing logic.
- The framework includes security measures like explicit opt-in for code execution, import restrictions, and post-condition verification to mitigate risks.
- Multi-agent composition allows AI Functions to chain naturally through Python, enabling complex workflows with validated results across sub-agents.
- Async execution in AI Functions supports parallel workflows, improving efficiency by running independent tasks concurrently.
- Configuration sharing via AIFunctionConfig objects allows different parts of a workflow to use different models, optimizing for cost and performance.
- AI-powered post-conditions can validate semantic qualities like grounding and citation quality, using one LLM to validate another.
- Existing test suites can serve as post-conditions, with the LLM iterating until all tests pass, improving implementation accuracy.
- AI Functions is an experimental project, open-sourced by Strands Labs, designed to explore the future of AI in software development.