Weave Engineering Intelligence

Start with one bounded use case matching Weave Engineering Intelligence's core job: Machine-learning analytics for measuring engineering work and identifying patterns that can improve software-team performance. Define the expected result before connecting broad accounts or importing large datasets.

Run a normal example and at least one difficult case. Inspect the AI output or completed action for unsupported assumptions, missing context, latency, and recoverability. When the product exposes logs, citations, traces, or previews, use them to understand how the result was produced.

Grant only the permissions required for the test. Keep human approval for production code, security changes, customer-facing communication, financial actions, health decisions, or other consequential operations. Verify what happens when an integration fails or the system is uncertain.

Once the workflow is reliable, save reusable instructions, policies, templates, or integrations and expand usage gradually. Re-test important workflows after major product or model updates.