Relevance AI is a platform for building agents, tools, knowledge, and multi-agent workforces. Strawberry can inspect the agent configuration in the browser and trace a run through its inputs and tools. It can compare output with the underlying knowledge and prepare a quality review before a workflow is trusted with a real business process.
01
Read the agent and the run together.
An instruction can look sound until a tool returns partial data, a knowledge source is stale, or a model takes an unexpected route through a task. Strawberry can pair the visible agent setup with its run evidence. The team can review behaviour rather than judge a workflow from its prompt alone.
02
Keep knowledge quality visible at the point of output.
An agent that answers fluently can still be operating from incomplete documents, outdated policy, or context that does not apply to the current request. Strawberry can compare the visible response with the knowledge it relied on and surface what needs a source owner.
03
Turn repeated failures into tests rather than folklore.
When an agent fails the same way twice, a team needs a reproducible evaluation or an explicit boundary, not another memory of what went wrong. Strawberry can collect visible failure patterns and prepare the cases that deserve a quality test or design decision.
04
Review the workforce before the weekend backlog grows.
A Friday afternoon pass catches broken runs, knowledge gaps, and configuration changes while the people who built the workflow can still assign a fix. It gives Monday’s operators a clearer starting point than a queue of unexplained agent output.
Relevance AI Strawberry