How to Duplicate AI Agent Workflows Across Accounts in HighLevel

Building conversational AI agents requires painstaking attention to operational detail. A single production-ready bot often combines custom system prompts, precise API call payloads, conditional branching, and fallback routines. Historically, replicating a proven prompt structure or conditional sequence for another client sub-account meant opening multiple windows and manually dragging, configuring, and linking every single node from scratch.

This manual reconstruction was not only inefficient, but it also introduced unnecessary operational risk. A single mistyped variable name or misrouted edge could break an entire conversation during live deployment. Recent updates to HighLevel's Agent Studio resolve this technical bottleneck by introducing native copy, paste, and bulk deletion capabilities directly inside the visual graph builder.

Selecting and Copying Agent Logic

Replicating logic inside the graph canvas now follows standard operating system keyboard shortcuts. Instead of recreating prompts and action nodes step by step, operators can capture individual elements or entire branches in a single action.

To select multiple elements simultaneously, click and drag a selection box across the desired nodes on the canvas. This highlights the entire sub-workflow, including prompt steps, conditional switches, and tool executions.

• Single Node Selection: Click any individual node to target its specific configuration.
• Multi-Node Selection: Click and drag across the canvas surface to encapsulate complex, multi-step logic branches.
• Copy Command: Press Cmd + C on macOS or Ctrl + C on Windows to save the highlighted selection to your system clipboard.

Pasting Workflows Across Canvas Tabs

Copied nodes can be duplicated within the active agent canvas or transferred directly into a completely different agent running in another browser tab. This cross-tab functionality allows agencies to maintain a master template account and deploy proven conversational modules into client sub-accounts in seconds.

When executing the paste shortcut (Cmd + V on macOS or Ctrl + V on Windows), the canvas automatically processes the underlying data structure to prevent configuration conflicts:

• Offset Placement: Pasted nodes land slightly offset from the current canvas selection so they do not overlap or obscure existing visual elements.
• Unique Node Identifiers: The system automatically assigns fresh system IDs to every pasted node, preventing variable collisions with the source graph.
• Preserved Internal Edge Routing: When copying a connected group of nodes, all internal links between those specific nodes remain intact upon pasting.

Relinking and Validating Pasted Nodes

Once a copied block is pasted onto the canvas, the final step involves integrating it into the primary conversational flow and verifying its variable mappings.

1. Position the pasted block in an open section of the canvas near the target entry point.
2. Connect the output edge of your preceding logic node to the input handle of the first node in the pasted sequence.
3. Route the exit paths of the pasted sequence to your downstream destination nodes or fallback handlers.
4. Inspect custom prompts, variable tags, and API parameters inside the pasted nodes to confirm sub-account accuracy.
5. Run a full test sequence in the built-in simulator prior to saving and publishing the updated agent.

Streamlining Canvas Cleanup with Bulk Delete

Iterative bot development frequently leaves behind orphan nodes, outdated testing pathways, and redundant logic branches. Previously, removing these elements required selecting each node individually and manually confirming its deletion.

The graph builder now supports bulk deletion. Highlight any combination of unused nodes using click-and-drag multi-selection, then press Delete or Backspace on your keyboard. The entire selection is removed instantly, clearing the visual canvas for easier debugging and ongoing maintenance.

Combining multi-node copying with instant bulk deletion significantly lowers the technical overhead of building and managing custom AI agents at scale. Operators can now treat successful conversation flows as modular building blocks rather than bespoke, single-use builds.