orchestration

A graph in the context of AI work is simpler than it sounds: it's a map of which jobs need to happen and what each one depends on.

We leverage probabilistic programming principles to tackle complex tasks, and utilize differentiable and classical programming paradigms with their respective strengths. The framework introduces a set of polymorphic,
compositional, and self-referential operations for multi-modal data that connects multi-step generative processes and aligns their outputs with user objectives in complex workflows. As a result, we can
transition between the capabilities of various foundation models with in-context learning capabilities
and specialized, fine-tuned models or solvers proficient in addressing specific problems. Through
these operations based on in-context learning our framework enables the creation and evaluation of
explainable computational graphs. Finally, we introduce a quality measure and its empirical score
for evaluating these computational graphs, and propose a benchmark that compares various stateof-the-art LLMs across a set of complex workflows.

That’s why we’ve decided to approach the software factory category as infrastructure rather than as a factory product or AI teammate.

a cloud software factory is just an automation around the core loop of development, from triage → spec → implement → review → verify → ship → monitor, where at every step a mix of agents and humans moves the process forward.

The core insight is this: AI agents don’t save you time by finishing your work. They expose how much work was always possible but never attempted. Five constraints now govern how much of that backlog any organization can actually capture: judgment, planning, coordination, evaluation, and absorption. Understanding those five constraints tells you exactly which new roles are being created and why.

Reacting to an event, running a sequence of isolated subagents, and separating their reasoning from the actions they’re allowed to take — it’s all just a workflow. One that could run just as well from a Slack message, a cron job, or a webhook as from a GitHub issue. Generalizing that realization into a runtime that works the same way regardless of where it’s deployed, or which model it’s driving, is what became Flue: an open, platform-agnostic framework for building durable agents and workflows.