n8n is a source-available automation platform that pairs a visual workflow editor with code-level representations. Although it operates as a managed platform, n8n is incredibly fast; a single instance can process up to 220 executions per second and supports horizontal scaling for enterprise use cases.
Teams typically rely on n8n to automate business workflows across HR, IT, finance, and sales. While agents in n8n can retrieve data, generate outputs, and trigger workflow branching, it is not an agent-first service. It is limited to single-agent workflows and provides relatively few prebuilt actions for integrating agents with third-party tools (e.g. Salesforce, Google Drive, Box).
Extensibility is where n8n excels, especially when integrated with platforms like Credal to enable multi-agent coordination, permission-aware data access, and human-in-the-loop governance.
Today, let’s explore the full spectrum: n8n’s purpose, how it works, and how tools like Credal unlock its multi-agent potential.
Why Zapier and Make weren’t enough for enterprise automation
n8n was founded in 2019 to address limitations in the automation market, which at the time was led by Zapier and Make. These services made it simple to connect different SaaS products, but weren’t built for long-term enterprise growth. As a result, teams would quickly encounter three recurring limitations in just a few months:
- Vendor lock-in: Workflows stopped being owned by teams and instead were controlled by the vendor’s pricing changes and platform decisions. For complex automation, the only options were to either accept constraints or start over from scratch.
- Limited customization: Pre-built connectors can handle basic scenarios, but custom logic for proprietary systems such as uncommon databases (or even other platforms) are locked behind enterprise fees.
- High cost: Plans that begin at $20/month rapidly escalate into thousands of dollars as usage ramps up. You end up paying a premium for infrastructure you could run yourself at a fraction of the cost.
n8n positioned itself as a viable alternative addressing these challenges with an open-ended design.
n8n’s visual and programmatic approach to automation
With a visual workflow builder and developer-level extensibility, n8n sidesteps the limitations of Zapier-like services. Its fair-code license guarantees transparency into the codebase while giving teams the freedom to extend and customize the platform to their needs.
- No vendor lock-in. Workflows are stored as JSON files, which teams can freely move or copy without permission or fees.
- Community-driven innovation. Enterprise security teams can audit the full codebase while open community contributors continuously submit new connectors and features, with no secrets or backdoors.
- Developer-first design. n8n can be managed via API, enabling teams to integrate freely into CI/CD workflows and programmatically control operations alongside the visual tools.
Because n8n is so adaptable, different teams can leverage it in unique ways. IT can handle authentication logic inside internal tools, marketing can automate targeted campaigns from start to end, and customer success can auto-route tickets in a helpdesk system with automated follow-up emails later on.
Two features make these sophisticated tasks possible in n8n: (a) n8n’s foundational node system and (b) n8n’s built-in AI integrations. Let’s dive into each.
n8n’s node system
Although people today may see n8n as an AI product, it's real strength lies in its node-based design. Each node in n8n is an individual component that executes a specific task. This modular architecture supports both straightforward automations and complex enterprise workflows.
Node Types and Data Flow
- Trigger nodes start workflow execution in response to webhooks, schedules, or external events. Webhooks allow any service to trigger a workflow without needing a specific app node. This makes it possible to build nested setups.
- Action nodes execute API calls, transform data, interact with databases, and run custom code. Their isolated nature is easier to debug.
- Flow control brings conditional logic, loops, and branching to create the automation patterns that are needed in for the complexity of real-world business processes.
Nodes pass data as structured JSON, providing developers with full visibility into the state of the data at every step. JSON is also widely recognized and readable, meaning developers can map transformations and align the request/return structure of standard APIS throughout the workflow.
Supporting all of this is a remarkably fast engine:
- A single n8n instance can execute up to 220 workflows per second, with response times around 20-50ms for basic webhook workflows.
- Redis-powered queue mode optimizes workflows by separating the backend into three parts: main process for UI and orchestration, worker processes for parallel task execution, and a Redis queue to distribute jobs. This design efficiently accommodates 10,000+ daily executions.
- Database support for SQLite in development, PostreSQL for production, and MySQL for enterprise setups with specialized requirements.
The high-performance infrastructure only scratches the surface of n8n’s value-proposition, as its main purpose today is to build AI-powered workflows.
n8n’s native AI integrations
n8n has nowadays positioned itself as an AI-first product. It offers a variety of nodes with AI capabilities, including summarization, document processing, and reasoning steps. Additionally, n8n natively integrates with LangChain, a widely used development framework for prompt chaining.
In this sense, n8n could be classified as a “low-code” tool. Rather than coding AI-powered systems from scratch, enterprises can leverage n8n to create structured, visual workflows that extract data, make decisions, and push that data to other systems. Enterprises do this in numerous ways:
- Onboarding new employees by automatically creating accounts, assigning permissions, and configuring notifications for new hires across HR, IT, and security systems.
- Converting natural language into API calls, where chat or text requests translate to automated API actions without any code.
- Enriching security incident tickets with relevant data and context automatically attached to security alerts, accelerating resolution times.
n8n agents can handle simple decisions in workflows, usually yes/no choices based on the context given. But they can’t dig deeper or ask questions before reaching a conclusion. This limitation does not diminish n8n’s AI automation capabilities. It does, however, emphasize the potential to extend n8n through integrations with specialized systems. For example, consider the use of Credal.
Credal is the non-deterministic node for a deterministic n8n workflows
AI features in n8n are restricted to the platform’s finite, deterministic decision trees. The nature of AI though, entails iterative exploration of data and numerous rounds of refinement until it can confidently make a decision.
Agents follow the platform’s deterministic approach: inputs flow in, the agent deliberates once, and an output is generated. There’s no ongoing reasoning or “thinking.” Still, this isn’t a drawback: n8n’s extensible architecture enables seamless integration with platforms like Credal.
Credal opens the door to fully non-deterministic agents. Take for instance, a Credal agent that can perform competitive analysis by iteratively collecting information from Salesforce, Google Drive, usage metrics, and Confluence while exploring multiple research paths to identify unique insights. Or, Credal could oversee deal flow by assessing inbound conversations, company profiles, and deal timing before triggering the next appropriate n8n workflow.
Think of n8n as the hands and Credal as the brain. n8n agents can repeat tasks like muscle memory, while Credal can act on any context and do almost anything. And this is made more powerful because Credal agents don’t operate in isolation.
Bringing multi-agent workflows to n8n
Another limit of n8n is that it only supports single-agent workflows. You can rig multi-agent interactions with hacky tool calls, but it is not officially supported and likely won’t ever be. The platform’s strength is its deterministic, graph-based workflow system. Multi-agent workflows, where multiple agents interact to solve problems, are inherently non-deterministic and cannot easily fit a graph-tree structure.
Due to its extensibility, n8n can pass off complex, multi-department cases to a system like Credal (e.g. checking if a data query meets compliance rules). By leveraging Credal, agents can discover one another; dynamically finding and activating each other to execute tasks. Rather than depending on a single generalist agent, Credal coordinates specialists that focus on a specific expertise and collaborate to solve complex problems.
An orchestrator agent might call a Salesforce curator agent and an analytics agent to assess whether a customer is at risk of churn based on account activity and interacts with the revenue team. The orchestrator can then trigger an n8n workflow to send either a re-engagement email or an upsell message based on the identified churn risk.
There is a catch with multi-agent coordination: agents can share information stored in memory, sometimes ignoring external access rules. Consider Agent A, which might have sensitive data that Agent B isn’t cleared for, which it shares due to the non-deterministic nature of agents. So when n8n hands off multi-agent work to another system, guardrails need to be in place to prevent such leakage. This is why platforms such as Credal provide these protections in-house.
Closing Thoughts
n8n offers enterprises a fast, extensible, and open-ended automation platform without the lock-in and escalating costs associated with services like Zapier and Make. But there is a growing need for probabilistic reasoning in enterprise automation.
Credal fills this gap for n8n as a reasoning layer. n8n runs the workflows with speed and portability, while Credal makes the decisions and manages agents. Together, they cover both execution and reasoning for enterprise needs.
Enterprises get AI workflows that are both reliable and fully compliant with SOC 2, HIPAA, and GDPR requirements.
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