A comparative study on agentic reliability — evaluating conventional model-driven loops against deterministic, event-sourced workflow engines.
"Long-horizon AI-agent reliability improves when stochastic model decisions are compiled into typed, deterministic, durable, verifiable workflows rather than executed through an unconstrained conversational tool loop."
The conventional ReAct-style loop used by most current agent frameworks.
A deterministic engine treating the model as a stochastic proposal generator within a formal state machine.
# Clone and install
git clone https://github.com/rmax-ai/durable-agent-runtime-lab
cd durable-agent-runtime-lab
uv sync --extra dev
# Run tests
uv run pytest # 256 tests
# Run an experiment (mock provider, no API key needed)
uv run dar experiment run --config experiments/configs/quickstart.yaml
# Run the same experiment with OpenAI (requires OPENAI_API_KEY)
UV_CACHE_DIR=.uv-cache uv run dar experiment run \
--config experiments/configs/quickstart.yaml \
--provider openai
# Generate the latest run report
uv run dar experiment report