Step 1: Define Your Agent’s Goal

Start by writing a clear mission for your agent in the first block, such as “classify incoming support tickets and draft a suggested reply.” The more specific your goal and your examples, the easier it is for Wordware to generate sensible scaffolding. This goal will drive the blocks you add next.

Step 2: Add the Inputs and Data

Connect your data source to the workflow. Wordware supports file uploads, API integrations, and manual inputs, so you can feed it documents, CSV rows, or live API responses. Keep this step narrow: give the Wordware agent only the fields it genuinely needs, because focused inputs produce more reliable outputs and lower token cost.

Step 3: Compose the Prompt and Logic Blocks

Drag in a prompt block and write the instruction your agent should follow. Use the visual canvas to add conditionals and loops — for example, “if the ticket mentions billing, route to refunds.” Because Wordware renders logic visually, both technical and non-technical teammates can read and adjust the flow without wading through code.

Step 4: Test Inside the Canvas

Before you deploy, run test cases directly inside Wordware. Add a few representative inputs and check the outputs for correctness. Use the built-in evaluation view to compare results and catch regressions when you tweak a prompt. This step is what turns a demo into something you can trust in production.

Step 5: Version, Deploy, and Monitor

Once your agent passes tests, save a version and deploy it. Wordware exposes your agent through an API endpoint your application can call. After going live, watch the logs to inspect every step’s inputs and outputs, then iterate on the prompt and release a new version. Because versioning keeps old builds safe, you can roll back instantly if a change underperforms.

Tips for a Better Visual Agent

Keep prompts short and concrete, add real examples, and test on the trickiest cases you can find. Treat your Wordware agent like a junior teammate: clear instructions, tight scope, and honest feedback loops give the best results. When in doubt, review the step logs to see exactly where the agent goes wrong before you edit the flow.