A conversational agent can fail because of an intent, training phrase, response, context, integration, or a gap in the user journey. Strawberry can work from the Google Dialogflow pages you open to turn those scattered details into a test plan, an incident review, or a clear change proposal.
Anchor every proposed improvement in a testable user path so the team can understand what the conversational agent would do differently.
01
Trace a failed conversation to the agent design that needs review.
Open the relevant Dialogflow console views and the conversation evidence, then ask Strawberry to build a diagnosis that distinguishes the observed user path from a proposed fix. This gives the owner a testable starting point.
02
Review intents and responses against the user journey.
Intent names and training phrases make more sense when seen alongside the user question and the response that followed. Strawberry can organise the visible agent context into a journey review that highlights ambiguity, dead ends, and the next content question.
03
Prepare a safe change proposal before a bot behaviour shifts.
Before changing a conversational response or routing rule, prepare a proposal that identifies what would change, which user path is affected, and how the team will test it. A browser review keeps the decision legible before anything reaches users.
04
Create a recurring conversation-quality check.
A repeatable Dialogflow quality pass can become a skill tied to your own taxonomy and escalation rules. Use a routine to prepare the review pack, then let the responsible team approve changes to the live agent.
No. Use a signed-in Dialogflow browser tab and ask Strawberry to help from the visible console context.
It can organise the agent views and conversation evidence you open into a diagnosis and test plan. Validate the technical conclusion in your environment.
Do not assume a browser workflow will make a live change. Keep the final configuration decision and rollout approval with the agent owner.
Yes. It can prepare a user-journey review that highlights visible ambiguity, missing handoffs, and questions to test.
Yes. Turn a proven review into a skill and use a routine to prepare the recurring evidence pack.
Check the specific user path, expected response, test cases, escalation impact, and rollback approach before a behavioural change reaches users.
Google Dialogflow Strawberry