Build AI graph workflows.
Prove they work.
LoopClimber treats the entire system—not one prompt or model—as the unit of improvement. Design the graph, measure its behavior, and let a cluster search for a better version.
Build AI graph workflows.
Define the graph contract.
Describe inputs, observable outputs, available data, tool permissions, state, budgets, risks, and human authority. The same contract can represent research, operations, support, engineering, or physical-world workflows.
Compose the workflow.
Connect models, agents, grounded retrieval, context filters, memory, tool calls, user interfaces, guardrails, retries, and recovery paths as explicit nodes. Nest reusable subgraphs without hiding their dependencies.
Encode quantitative evals.
Turn requirements into a test DAG with held-out cases, thresholds, tolerances, samples, and fail-fast rules. Track task success, grounded-claim precision, tool errors, p95 latency, cost per successful run, and unsafe actions—then join every required gate.
Run the graph on a cluster.
Set a flow rate on every edge, execute independent branches in parallel, and expose backpressure. When a dependency blocks, work queues visibly; recovery drains the stack and restores blue throughput without losing deterministic order.
Hill-climb the whole system.
Create candidate nodes and edges, update their tags as quantitative results resolve, and delete unsafe or dominated branches. Repeat matched trials and turn every failure into a permanent regression eval.
Promote what survives.
Ship the winning graph with its configuration, eval manifest, timing envelope, traces, controls, and regression bank. Set up a LoopClimber cluster around your workflows and test cases.