Archive chat summaries upon human agent handoff

Whenever a conversation is handed off in Continually, automatically write a summary directly into MySQL for central analytics.

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How Knit builds this workflow

The Agent researches both APIs and wires the trigger on one side to the action on the other — no pre-built connector required. Here is the vocabulary it has to work with.

Continually Explore Continually →

Triggers

New conversation started

Fires when a new chat or conversation begins in Continually.

Lead captured in chat

Fires when Continually captures a lead during a conversation.

Conversation handed off

Fires when Continually escalates a conversation to a human.

Actions

Send a message

Sends a message to a user through Continually.

Generate an answer

Asks Continually a question against its knowledge base and returns the answer.

Summarize a conversation

Uses Continually to summarize a chat transcript.

Create a ticket from a chat

Opens a support ticket from an Continually conversation.

MySQL Explore MySQL →

Triggers

New row inserted

Fires when a new row is added to a table in MySQL.

Row updated

Fires when an existing row changes in MySQL.

Query result ready

Fires when a scheduled query in MySQL finishes running.

Actions

Run a query

Runs a SQL query against MySQL and returns the result.

Insert or update a row

Writes a new or updated row to MySQL.

Sync a table

Syncs a table in MySQL with data from another source.

Store new chat leads in a backend database When a prospective customer provides details in Continually, automatically insert or update a row in MySQL to retain prospect data. See details

Answer chatbot inquiries using database query results When a data query result becomes ready in MySQL, automatically send a generated message in Continually back to the active user. See details

See all Continually + MySQL integrations → · Browse the full workflow library →

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FAQ

Common questions

Can Knit build “Archive chat summaries upon human agent handoff” between Continually and MySQL?

Yes — describe it in the box above and Knit's Integrations Agent researches Continually and MySQL's public API docs (or your own uploaded docs) and builds a working workflow, whether or not either app already has a pre-built connector.

How long does it take to build?

Minutes to a first working version, not weeks — you test it against real data before it goes anywhere near production.

What is the main benefit of pairing Continually with MySQL?

It bridges public customer chats with backend data stores, ensuring lead details are stored safely and allowing assistants to retrieve live record updates instantly.

Can updates in MySQL prompt messages in Continually?

Yes, when a row is updated or inserted in MySQL, you can instruct Continually to send an automated message or follow-up response to the user.