What is an agent orchestration platform?

The platform where you define and execute your AI agent workflows. It connects the AI model to your business processes and controls which steps run in which order, when humans approve, and how errors are handled.

When should I use n8n vs. Camunda?

n8n for fast results and prototyping: visual editor, native AI nodes, productive in two weeks. Camunda for compliance-critical processes: BPMN 2.0, human tasks, complete audit trail, versioned processes running for weeks or months.

Can n8n and Camunda be used simultaneously?

Yes. Many companies use n8n for quick internal automations and Camunda for audited core processes. Both can interoperate via APIs and webhooks.

Agent Orchestration: n8n, Camunda, and Alternatives Compared

You Know What Agents Are. Now: Where Do They Run?

You understand what AI agents are, how they communicate with your systems via MCP and A2A, and how the Decision Layer controls which decisions they may make. The next question is concrete: on which platform do you orchestrate these agents? Where do you define the workflows that connect a language model with your business processes?

The answer depends on where your organization stands. Do you need fast results with visual workflow automation — or do you have complex, long-running business processes that require BPMN-compliant control and audit-ready documentation?

This article compares the five most relevant platforms, explains the fundamental architectural distinction between visual workflow tools and process orchestration engines, and provides a practical recommendation for getting started.

Two Worlds of Orchestration

The market for workflow orchestration in 2026 divides into two categories that solve different problems.

Visual Workflow Automation

Platforms like n8n, Make, and Activepieces follow the visual workflow paradigm. Workflows are assembled by clicking nodes together in a canvas editor. A trigger — a new email, a new document, a webhook — starts a chain of actions, including calls to AI models. The advantage: fast results, low barrier to entry, usable even by non-developers. The disadvantage: when processes involve dozens of decision points, parallel paths, human approval loops spanning multiple days, or compliance requirements for versioning and audit, visual tools reach their limits.

Visual workflow tools are optimized for sequences: trigger, action, action, action, done. They handle simple branching well. What they handle less well is the concept of a process that waits — for days or weeks — at a defined point for a human to approve a step before continuing.

Process Orchestration Engines

Platforms like Camunda and Temporal take a fundamentally different approach. Processes are defined as formal models — BPMN diagrams in Camunda’s case, code in Temporal’s. Every step is versioned and auditable. Human tasks — points in the process where a human must review, approve, or decide — are a core part of the architecture, not an afterthought.

The advantage: enterprise-grade compliance, native audit trail, processes that run for weeks or months with full traceability. The disadvantage: higher entry barrier, requires developer expertise, and more upfront design effort.

The distinction is not about quality. It is about fit. A visual tool that produces results in two days is superior to a BPMN engine that produces results in two months — if the use case does not require audit-grade compliance. Conversely, a BPMN engine that provides full traceability is non-negotiable when the works council (Betriebsrat), auditors, or regulators need to trace every decision.

Platforms Compared

The following table provides a structured comparison of the five most relevant orchestration platforms for AI agent workflows in 2026.

Property	n8n	Camunda	Make	Temporal	Activepieces
Type	Visual Workflow	BPMN Process Engine	Visual Workflow	Code-first Orchestration	Visual Workflow
Self-Hosted	Yes (Docker, k8s)	Yes (Self-Managed) or Cloud	No (SaaS only)	Yes (Docker, k8s)	Yes (Docker)
AI/LLM Integration	Native AI nodes (Ollama, LangChain)	Connectors for LLM APIs, custom workers	AI modules	Any LLM in worker code	AI pieces
Key Strength	400+ integrations, visual editor, fast prototypes	BPMN 2.0, human tasks, audit trail, long-running processes	Simplest entry, intuitive visual builder	Durable execution, retry/timeout native, maximum reliability	Open-source alternative to n8n/Make
Enterprise Fit	Medium—High	Very High	Low—Medium	High	Medium
License	Fair Source (free <3 instances)	Community (Apache 2.0) + Enterprise	Proprietary (SaaS)	MIT (Core) + Commercial (Cloud)	MIT

Each platform has a distinct profile. The choice is not about finding the “best” platform but about matching the platform to your requirements.

When to Use Which Platform?

n8n — When You Need Fast Results

You need a functioning agent in two weeks that extracts invoices from an email inbox, classifies them via a language model, and books them into your ERP system? n8n is your choice.

The visual editor enables building workflows without traditional programming. AI nodes are natively integrated — you can embed a language model into the workflow like any other service. Self-hosting with Docker means your data does not leave your network. The community is active, and with over 400 integrations, connections to most enterprise systems are available out of the box.

The trade-off: n8n workflows excel at sequential automations and simple branching. For processes with parallel paths, human approval loops spanning multiple days, or compliance requirements for versioning and formal audit, n8n reaches its limits. It was designed as a workflow automation tool, not as a process engine. For many AI agent use cases — particularly those in the proof-of-concept phase — that distinction does not matter. For production processes with regulatory requirements, it does.

Best for: Rapid prototyping, internal automations, PoC agents, teams that want visible results quickly.

Camunda — When Compliance Is Non-Negotiable

Your works council (Betriebsrat) wants to trace which agent made which decision? Your auditors need a complete audit trail? The process runs for weeks — onboarding, contract approval, regulatory submissions? Then you need a BPMN engine.

Camunda models processes as formal BPMN 2.0 diagrams that any business stakeholder can read — including the works council. Human tasks are a core concept: at defined points, the process waits for human approval. Every decision is versioned and traceable. AI agents are integrated as service tasks — the language model becomes a step in the business process, not a black box.

The BPMN standard is an international norm (ISO 19510). Diagrams are portable, readable by external auditors, and serve as documentation that satisfies both technical and organizational requirements. When a company agreement (Betriebsvereinbarung) defines that certain agent decisions require human oversight, Camunda enforces this architecturally.

Best for: Compliance-critical processes, long-running workflows, regulated industries, organizations with works council requirements.

Temporal — When Your Developers Need Maximum Control

Temporal is code-first. Workflows are written in Go, Java, TypeScript, or Python. No visual editor, but maximum control over retry logic, timeouts, and error handling. For AI agents that execute complex, multi-step tasks with unpredictable runtimes — document analysis with follow-up queries, multi-system orchestration with conditional branching — Temporal is the most robust choice.

Temporal’s concept of durable execution means that a workflow survives infrastructure failures. If a server goes down mid-process, Temporal resumes exactly where it stopped — without data loss, without duplicate actions. For AI agent workflows where a single execution may involve multiple LLM calls, each with variable latency, this reliability is architecturally significant.

The trade-off: Temporal requires developers. There is no visual designer, no drag-and-drop interface. Non-technical stakeholders cannot read or modify workflows directly. For organizations with strong engineering teams, this is acceptable. For organizations that need business users to participate in workflow design, it is not.

Best for: Engineering-led organizations, complex multi-step agent workflows, scenarios requiring maximum reliability and fine-grained error handling.

Practical Recommendation: n8n for PoC, Camunda for Production

Most organizations follow a predictable pattern. They start with n8n because it delivers visible results fast. The first agent runs in two weeks, stakeholders see the value, and the business case writes itself. Then governance requirements arrive: the works council asks how decisions are traced, auditors ask for documentation, the legal department asks about EU AI Act compliance.

At that point, n8n is frequently complemented or replaced by Camunda — not because n8n is inadequate, but because the requirements have grown beyond what a visual workflow tool is designed to handle.

The practical recommendation: start with n8n for the proof of concept. Get the first agent running, measure results, demonstrate value. Then add Camunda for production processes that require formal audit trails, human tasks, and versioned process models. The two platforms can coexist: n8n for fast, internal automations and Camunda for the audited core processes. Both communicate via APIs and webhooks.

This is not a compromise. It is a deliberate architecture that matches the right tool to the right requirement. Speed where speed matters. Compliance where compliance matters.

For organizations that need an open-source alternative without licensing concerns, Activepieces (MIT license) offers a viable starting point for visual workflows. For engineering teams that want full code-level control, Temporal fills the gap between visual tools and BPMN engines.

Integration with the Agent Architecture

The orchestration platform is the execution layer in the agent architecture. It connects the components that together form a functioning AI agent system:

┌─────────────────────────────────────────────┐
│         Enterprise AI Portal                │
│    (LobeChat / OpenWebUI / very-ai)         │
└──────────────────┬──────────────────────────┘
                   │
┌──────────────────▼──────────────────────────┐
│       Orchestration Platform                │
│    (n8n / Camunda / Temporal)               │
│                                             │
│  ┌─────┐  ┌─────────┐  ┌────────────┐      │
│  │Step │→ │  LLM    │→ │ Decision   │→ ... │
│  │  1  │  │ Analysis│  │   Layer    │      │
│  └─────┘  └─────────┘  └────────────┘      │
└──────────────────┬──────────────────────────┘
                   │
    ┌──────────────┼──────────────┐
    ▼              ▼              ▼
┌────────┐  ┌──────────┐  ┌──────────┐
│  RAG / │  │  ERP /   │  │ Email /  │
│ Vector │  │  CRM     │  │ Calendar │
│   DB   │  │          │  │          │
└────────┘  └──────────┘  └──────────┘

The Enterprise AI Portal is the interface through which users start agents. The orchestration platform defines the workflow: which steps, in which order, under which conditions. The AI model performs the analysis — understanding text, extracting documents, preparing decisions. The Decision Layer controls which decisions are made automatically and which require human approval. RAG provides the model with context from your enterprise documents.

The orchestration platform sits at the center. It is the layer that turns an AI model from a question-answering tool into a process-executing agent. Without it, the agent has no structure. With it, the agent has a defined workflow, error handling, escalation paths, and a traceable execution history.

Choosing the right orchestration platform is not a technology decision alone. It is an architecture decision that affects governance, compliance, and the speed at which you can move from pilot to production. Get this layer right, and every subsequent agent builds on a solid foundation. Get it wrong, and you will rebuild when the first compliance audit arrives.

Gosign builds AI agent infrastructure with the orchestration layer that matches your requirements — from rapid n8n prototypes to fully audited Camunda production processes. The infrastructure is designed so that the first agent is simultaneously the foundation for every agent that follows.

📘 Enterprise AI Infrastructure Blueprint 2026 – Article Series

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