Building Tips
Building approaches
We observe that our most successful users follow one of two building approaches:
Input-led: Start with your available data (e.g., image prompt, brand guidelines) → build forward by adding nodes that transform inputs into marketing assets.
Output-led: Start with your desired output (text, image, video) → select the right AI node → work backwards to determine what inputs you need.
Below are some additional best practices for building custom Pletor agents:
Start simple: Begin with a basic workflow and add complexity gradually.
Test as you build: Run individual nodes and group of nodes frequently to test them in isolation and catch issues early.
Use AI text nodes with custom instructions to:
enhance prompts
process multiple inputs at the same time (e.g., user prompt + brand context)
stabilize outputs' quality that will serve as inputs to other nodes
Use tools like Prompt Cowboy to generate custom instructions in a few seconds.
Iterate on models and configuration. Adjust node settings, try different connections, and refine your workflow until you get the results you want. The AI Studio makes experimentation fast and visual.
Organizing your canvas
As agents grow in complexity, a well-organized canvas saves time and prevents mistakes.
Sticky Notes
Use Sticky Note nodes to document your workflow directly on the canvas. They don't affect execution — they're purely for communication.
Good uses for Sticky Notes:
Explain why a node chain is structured a certain way
Leave instructions for teammates who'll edit the agent
Mark sections that are work-in-progress or need review
Document which models or settings work best after testing
Name your nodes
Click any node's title to rename it. Descriptive names like "Cinematic Video" or "Fixed Prompt Instruction" make your flow readable at a glance — much better than a row of generic "Generate Image" labels.
Group related nodes
Select multiple nodes and group them to create labeled sections on your canvas (e.g., "Brand Inputs," "Video Production"). Groups make complex agents scannable, help teammates understand the flow without clicking into every node, and can be run as self‑contained steps.
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