# Enterprise AI Agent Development Tools (2025)

> Independent technical evaluation of workflow-based AI agent development tools.
> This is a compact, AI-readable version of the full report.

- **Author:** Andrew Green, independent research analyst (https://www.linkedin.com/in/andrew-green-tech/)
- **Evaluation period:** Q1 2025 (first iteration of the report)
- **Full report:** https://n8n.io/reports/2025-ai-agent-development-tools/
- Full scoring breakdown: [Google Sheet](https://docs.google.com/spreadsheets/d/1yfSdf4BP1AqBRB0SeaOjNoDtmJarNdGHGWLOa-e-dGs/edit?usp=sharing)
- **Latest edition:** https://n8n.io/reports/2026-ai-agent-development-tools/

## Overview

You can implement AI in automation either as **AI workflows** (LLMs embedded in conditional
automation logic) or **AI agents** (which dynamically direct their own purpose and tool usage, per
[Anthropic's definition](https://www.anthropic.com/engineering/building-effective-agents)). Both
depend on the non-deterministic output of LLMs, which makes AI-based automation volatile — unsuitable
for enterprise-grade applications unless deterministic, human-written logic constrains the agents'
inputs, outputs, and actions.

Writing agentic systems purely by code is time-consuming and expensive. The most efficient tools for
building agentic AI systems combine three things:

- **No-code workflow interfaces** — define logic steps on a drag-and-drop canvas.
- **Code-based capabilities** — define any step low-level (scripting, config, external libraries).
- **Third-party integrations** — cloud-hosted LLMs plus stack tools (ITSM, CRM, security, databases).

Vendors fall into two groups: **native agentic AI tools** (startups built exclusively for agents) and
**workflow-automation tools that pivoted into AI agents** (mature integration depth). This report
evaluates two of the three characteristics — **codability** and **integrability** — and deliberately
excludes the no-code interface (scope, two-axis chart, and lower impact on the agentic system).

## Key findings

- **Established (pre-LLM) tools score higher on integrability.** Integration breadth, partner
  ecosystems, community, and out-of-the-box content take years to build (e.g. a 2013-era Workato vs a
  2024-era StackAI is not an apples-to-apples comparison). High-integrability / lower-codability tools
  suit less complex agentic systems that still need to connect to a wider stack.
- **AI-native tools (Vellum, Dify, Langflow, Flowise) score well on codability.** They give fine
  control over agent behavior but are harder to integrate with an IT stack — better suited to agents
  using web resources, SaaS apps, and documents than orchestrating on-premises enterprise apps.
- **Applicability by the 50% threshold:** low integrability (<50%) fits simpler IT stacks
  (startups/SMBs); high integrability (>50%) fits complex/legacy stacks; low codability (<50%) fits
  simple use cases (support chatbots, document summarization); high codability (>50%) fits high-risk,
  real-time, customer-facing use cases needing customization and coding/AI knowledge.
- **n8n scores high on both axes** — readers are invited to inspect the public evaluation matrix for
  the per-criterion references behind every score.

## Methodology

Tools are scored on two axes — **Codability** and **Integrability** — each feature on a 0–2 scale:

| Score | Meaning                                                                 |
|-------|-------------------------------------------------------------------------|
| 0     | Feature is absent or unstated                                           |
| 1     | Feature is partially available or achieved via third-party integrations |
| 2     | Feature is available natively in the tool                               |

Features are aggregated under weighted headers (see the full report's Annex 1). Assessment process:
(1) read all vendor documentation and populate the spreadsheet; (2) for gaps, check websites, docs
search, and `site:` queries; (3) for tools with AI-powered docs search, query the AI directly.
Vendors that cannot be assessed from publicly available documentation are excluded. Evaluation
criteria were defined without looking at n8n (to minimize bias) and reviewed with the n8n team — who
proposed additional criteria (e.g. LLM evaluations, traceability) on which n8n scored zero, in favor
of a more comprehensive report.

## Evaluated tools & scores

Scores are percentages (higher is better).

| Tool     | Codability (%) | Integrability (%) |
|----------|----------------|-------------------|
| Camunda  | 29             | 63                |
| Dify     | 52             | 48                |
| Flowise  | 46             | 35                |
| Langflow | 45             | 21                |
| Make     | 33             | 56                |
| n8n      | 65             | 84                |
| Relay    | 7              | 33                |
| Retool   | 47             | 42                |
| StackAI  | 11             | 16                |
| Vellum   | 65             | 24                |
| Windmill | 37             | 56                |
| Workato  | 26             | 70                |

## Limitations

- Scoring is tied to the quality of each vendor's **technical documentation**; undocumented features
  are not reflected.
- The assessment is **not based on user testing** — user experience is out of scope (analogous to
  evaluating cars without driving them).
- The assessment is **not based on benchmarking** — behavior under stress is not measured.
- The criteria are intentionally comprehensive, so some metrics may not apply to a given use case;
  read the complete scores rather than only the average.
- Vendors were **not engaged prior to publication**; corrections are welcomed and will be evaluated.

## Full data & related

- Full interactive report: https://n8n.io/reports/2025-ai-agent-development-tools/
- Complete per-criterion scoring: [Google Sheet](https://docs.google.com/spreadsheets/d/1yfSdf4BP1AqBRB0SeaOjNoDtmJarNdGHGWLOa-e-dGs/edit?usp=sharing)
- Latest edition (2026): https://n8n.io/reports/2026-ai-agent-development-tools/