n8n makes it possible to connect applications, automate repetitive processes, and build powerful AI-powered workflows without having to manage every step manually.
But as teams start using more AI agents, another challenge appears: how do you manage the actual development work performed by those agents?
For example, imagine a team building an AI-powered customer support system with n8n.
The workflow might involve:
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Receiving incoming customer requests.
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Processing information with an AI model.
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Retrieving relevant data from external services.
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Routing requests based on their content.
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Sending automated responses or escalating complex cases.
Building this workflow is one part of the job. Maintaining it, improving it, assigning development tasks, and reviewing changes is another.
Where Sharkly.ai fits into this workflow
This is where Sharkly.ai can complement an automation environment such as n8n.
Sharkly.ai is an AI-native development workspace designed to bring human developers and AI agents together.
Instead of focusing on connecting application nodes, it focuses on coordinating the development work itself.
Teams can use Sharkly.ai to:
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Assign development tasks to AI agents and human teammates.
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Coordinate multiple agents working on different tasks.
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Track task execution and progress.
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Reuse agent configurations, instructions, and Skills.
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Review agent results before accepting changes.
For example, a team could use n8n to automate customer support processes while using Sharkly.ai to organize development tasks such as improving API integrations, updating workflow logic, writing tests, and documenting changes.
The two tools address different layers of the same broader process:
| n8n | Sharkly.ai |
|---|---|
| Connects applications and services | Coordinates developers and AI agents |
| Automates business processes | Organizes development tasks |
| Executes workflows | Tracks agent task execution |
| Integrates APIs and AI services | Supports reusable agent configurations |
| Automates operational processes | Keeps human review in the development workflow |
Why this distinction matters
Adding more AI agents to an automation project doesn’t automatically make the development process more efficient.
Teams still need to understand who is working on what, whether tasks are progressing, and whether the results are ready for review.
n8n helps automate the processes. Sharkly.ai helps organize the people and agents building and improving those processes.
A question for the n8n community
How are you currently managing the development side of your AI automation projects?
Do you use n8n to trigger coding agents, or do you keep workflow automation and AI-assisted software development separate?
I’d be interested in hearing how other teams coordinate multiple AI agents when building and maintaining complex n8n workflows.