What is n8n: the advantages, the limits, and how to support multi-agents with Credal

n8n is a source-available workflow automation platform that combines visual workflow building with exposed code-level representations. Despite being a managed service, n8n is extremely fast; it can handle up to 220 executions per second on a single instance and scales horizontally for enterprise workloads.

Today, teams use n8n to automate business processes that span HR, IT, finance, and sales. Agents in n8n can gather data, produce outputs, and trigger forks in workflows. However, n8n is not an agent-first platform. It only supports single-agent flows and has limited prebuilt actions to connect agents with third-party products (e.g., Salesforce, Google Drive, Box).

Where n8n shines is extensibility. It can integrate with systems like Credal that add multi-agent coordination, permission-aware data access, and human-in-the-loop governance. Today, we want to cover the full spectrum: why n8n exists, how it works, and how platforms like Credal extend its multi-agent capabilities.

Let’s get started.

Why Zapier and Make weren’t enough for enterprise automation

While n8n today is branded as an AI-first workflow automation tool, it was originally founded in 2019 to tackle the automation market, a space dominated by Zapier and Make. These tools made it easy to connect SaaS products, but left gaps that made it difficult for enterprises to scale. Within months, teams would often hit three predictable walls.

1. Vendor lock-in: Workflows would become hostages to pricing changes and platform decisions. When you need sophisticated automation, you're forced to either accept limitations or rebuild everything.
2. Limited customization: Pre-built connectors work for basic use cases, but if you need specialized logic for proprietary systems like uncommon databases (or another platform altogether), you’re locked out unless you pay enterprise fees.
3. High cost: What starts at $20/month quickly balloons to thousands as execution volume grows. You’re paying premium prices for infrastructure you could run cheaper yourself.

n8n positioned itself as an alternative by addressing these constraints through a deliberately open-ended design.

n8n’s visual and programmatic approach to automation

n8n blends a visual workflow builder with developer-level extensibility. It operates under a fair-code license, which enforces transparency into the codebase and allows teams to build on top of n8n’s core. This model counters the constraints of Zapier-like tools in several ways.

• No vendor lock-in. Workflows are portable JSON files. Teams can move or replicate them without permission or ransom fees.
• Community-driven innovation. Enterprise security teams can audit the entire codebase while open community contributors add connectors and features. There are no hidden behaviors or backdoors.
• Developer-first design. Every aspect of n8n is controllable via API, which allows for CI/CD integration and programmatic control alongside visual tools.

Because of its extensible nature, n8n can serve multiple roles. For example, an IT team might encapsulate it inside internal tools to handle authentication logic. Or a marketing team could orchestrate  campaigns that query systems to find target customers, and then use the customer data to execute on outreach. Or the customer success team might use n8n to automatically route tickets in a helpdesk system based on keywords or customer priority, including sending follow-up emails after ticket resolution.

These complex jobs are made possible by two distinct features of n8n: (a) n8n’s rudimentary node system and (b) n8n’s native AI integrations. Let’s discuss these in-depth.

n8n’s node system

While today, many people might think of n8n as an AI product, n8n’s design philosophy is more about nodes. Nodes in n8n are discrete components that perform specific actions. This modular approach handles both simple automations and complex enterprise processes.

Node Types and Data Flow

• Trigger nodes initiate workflow execution based on webhooks, schedules, or external system changes. Webhooks can trigger workflows from any service without dedicated app nodes. This allows for nested setups.
• Action nodes perform the actual work: API calls, data transformation, database operations, or custom code execution. Their isolation makes workflows easier to debug and maintain.
• Flow control provides conditional logic, loops, and branching for sophisticated automation patterns that handle real-world business complexity.

Data, meanwhile, is passed between nodes in a structured JSON format. This strategy is ideal for developers since they can see the full state of the data at each step. Additionally, because JSON is a well-understood and easily readable format, developers can map transformations and match the request/return structure of conventional API calls at any step of the workflow.

Additionally, this structure is backed by an extremely fast engine. n8n’s benchmarks are impressive:

• Single-instance performance reaches 220 workflow executions per second, with response times typically 20-50ms for basic webhook workflows.
• For enterprise workloads, Redis-powered queue mode splits your backend into three areas: main process for UI and orchestration, worker processes for parallel execution, and a Redis queue for job distribution. This architecture handles 10,000+ daily executions efficiently.
• Database support includes SQLite for development, PostreSQL for production workloads, and MySQL for enterprise deployment with specific requirements.

However, n8n’s performant infrastructure is only the base of its value-proposition. Its main purpose, today, is to create AI workflows.

n8n’s native AI integrations

Today, n8n is positioned as an AI-first product. To n8n’s credit, the platform is packed with nodes with AI integrations. There are nodes for summarization, document processing, and embedding reasoning steps. n8n also has a built-in integration with LangChain, a popular framework for developers to carryout prompt chaining.

In this context, n8n could be considered a “low code” product. Instead of building an AI-powered system through code, enterprises can instead use n8n to create organized and visual workflows that extract information, make decisions, and push data to systems. There are abundant versions of this in enterprises. Some common examples include:

• Onboarding new employees involves account creation, permissions, and notifications for new hires that are automated across HR, IT, and security systems.
• Converting natural language into API calls, where chat or text requests become automated API actions without manual developer intervention.
• Enriching security incident tickets, where relevant data and context are automatically added to security alerts for faster resolution.

Agents in n8n can also make lightweight decisions within workflows. But their reasoning is shallow. They typically resolve yes/no branches based on the context provided to them and cannot continue to ask questions or probe further until they are fully confident with their decision. That said, this limit does not reduce n8n’s role as an AI-powered automation engine. Instead, it highlights the opportunity to tap n8n’s extensibility and integrate with more specialized systems. Let’s discuss how by using Credal as an example.

Credal is the non-deterministic node for a deterministic n8n workflows

n8n is a powerful workflow system. However, its AI features are bound by n8n’s finite, deterministic decision trees. This is contrary to the nature of AI. AI shouldn’t be limited to a single round of deliberation; instead, it should progressively research and query data until it is confident with its decision.

While n8n has support for agents, they follow the same deterministic paradigm as the rest of the platform.  They take a single round of inputs from the flow or other third-party data sources, deliberate, and produce an output. Put simply: they don’t “think”. However, this isn’t a limitation in the grand scheme: n8n was designed for extensibly and neatly integrates with platforms like Credal.

With Credal, you can launch unbounded, truly non-deterministic agents. For example, Credal agents could generate a competitive analysis by iteratively gathering information from Salesforce, Google Drive, usage data, and Confluence, pursuing different research paths until they find differentiating insights. In another case, Credal could manage deal flow by evaluating inbound conversations, company profiles, and deal timing; then decide the next step and trigger the appropriate n8n workflow.

By analogy, if n8n are the hands, then Credal is the brain. It’s a particularly apt analogy, because even hands have muscle memory to do simple things like play the piano, much like n8n agents that rely on repetition over complexity. But Credal is like a brain that can do quite literally anything based on context. And this is only possible because Credal agents don’t work alone.

Bringing multi-agent workflows to n8n

Another constraint of n8n is its support for only single agents. So while multi-agent interactions are possible via hacky tool calls, n8n does not have first-party support for multi-agent orchestration. The platform likely never will build in this direction—n8n’s strengths are its rigid workflow system. Multi-agent workflows, where multiple agents talk to reach a solution, are nondeterministic and don’t follow a graph-tree structure.

However, n8n can delegate complex, multi-department work to a system like Credal (e.g. “determine if this data query follows the company’s compliance rules”). Using Credal, agents are discoverable to each other; discoverability means that agents can dynamically find and call each other to solve a problem. Instead of relying on one generalist agent, Credal coordinates specialists. This produces better results: agents can focus on narrow tasks and consult one another to complete more complex goals.

For example, an orchestrator agent might delegate to a Salesforce curator agent and an analytics-focused agent to evaluate whether a customer is at risk of churn based on account usage and interactions with the revenue team. If the customer is a churn risk, the orchestrator could trigger an n8n workflow to send a re-engagement email; if not, the workflow might send an upsell message instead.

There is a caveat: multi-agent coordination draws risk, as agents can share data stored in their memory context, bypassing external access rules. In other words, Agent A might have information that’s not authorized for Agent B, and that information gets shared due to the non-deterministic nature of agents. Accordingly, if n8n delegates multi-agent work to an external system, there also needs to be guardrails to prevent that system from leaking data. In platforms like Credal, these guardrails are pre-built.

Closing Thoughts

n8n has proven itself as an execution platform that avoids the lock-in and cost traps left by other platforms like Zapier and Make. It is fast, open-ended, and extensible. But enterprise automation increasingly requires reasoning that is probabilistic.

Credal complements n8n by being that reasoning layer. The result is a division of labor that maps cleanly to enterprise needs. n8n is responsible for executing workflows with speed and portability, while Credal decides and governs at the agent layer.

For enterprises, this means AI workflows that are both operationally reliable and compliant with SOC 2, HIPAA, and GDPR boundaries.