Enterprise AI agent development tools 2025

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A technical evaluation of workflow-based automation tooling for building enterprise-grade agentic systems using LLMs. Conducted by independent research analyst Andrew Green in Q1 2025
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If you want to implement AI in your automation processes, you can choose between AI workflows and AI agents - as defined by the folks at Anthropic - where workflows implement LLMs into conditional automation logic, while agents dynamically direct their own purposes and tool usage.
Both AI Workflows and AI Agents depend on the non-deterministic outputs of large language models. These varied outputs make AI-based automation volatile, a behavior that is not suitable for enterprise-grade applications. To address the risks associated with unpredictability and to enable more complex reasoning flows, you need to define deterministic logic to control the agents’ inputs and outputs. This is a natural part of AI workflows, but Agents should also be subject to conditional logic written by humans.
I write this evaluation with the perspective that AI Agents have self-determination, but their inputs, outputs, and actions are confined by a set of acceptable use rules written by humans.
While the most common way of defining deterministic logic is by coding, writing agentic systems from scratch just by coding is both time-consuming and expensive. This is a major deterrent, especially if you intend to bring your AI features to market sooner, and/or have not seen the returns on any AI features deployed so far.
Almost all vendors that I’ve encountered which position themselves as some flavor of agentic AI development tools have all of the above. I found it interesting that almost every tool on the market has opted for a no-code workflow development GUI. This is to be expected from tools that have been around before AI agents were a (big) thing, but it’s also the case for new tools. I believe this is because no-code workflow-based automation is the best way to write deterministic logic with minimal investment and training. Considering the above, I can categorize vendors in this category into two groups:
These are mostly startups that have built their platform extensively (and exclusively) to build AI agents
These vendors found themselves in a great position to hop onto the biggest bandwagon of the 2020s.
This is by no means an exhaustive list. For example, I found some tools that position themselves as AI agent development tools which do not use no-code/low-code workflow based automation. These include tools such as LangSmith, Crew AI, Restack, and Writer.com. At the time of writing, I do not have more information on how these three compare with the vendors included in this report, but if you are interested in developing AI agents without the workflow-based automation component, I would encourage you to look into these tools as well.
Workflow automation tools have some distinct maturity advantages, particularly around depth and breadth of integrations, which provide tried-and-tested methods of bringing a tech stack together without writing custom connectors for each application. Native Agentic AI dev tools, on the other hand, have the agility advantage and don’t have to retrofit AI to an existing product.
This piece of research aims to evaluate how extensive tools’ capabilities are for writing agentic AI systems and integrating them into an organization’s stack. The evaluation criteria revolve around coding capabilities and configuring LLMs and associated frameworks, and the tools’ integrability as measured by out-of-the-box content, ease of use, and reliability.
In short, we’re evaluating codability and integrability capabilities for writing AI agents.
This means that we’re only evaluating two out of the three characteristics used above to define what an Agentic AI development tool is. We’re not assessing the no-code interface component for three reasons. First, the scope would be too large. Second, we’re using a cartesian chart with two dimensions and would be unfeasible to introduce the third. Last, the range of capabilities for the no-code interface will not have as much of an impact on the agentic system as the codability and integrability component.
If you are in between two tools that are comparable for codability and integrability, the extensiveness of the no-code interface will be a great tie breaker.
Many - if not all - of these tools are Turing complete, which means they can perform any computation. This makes it difficult to prove or disprove a tool can do something, because a Turing complete tool can do anything. The same goes for integrations, where I often hear that “the tool can do anything because it integrates with APIs”.
While this is nice, it makes it difficult and frustrating to select one tool over another. So to circumvent some of these gotchas, all the definitions in this report will tend as much as possible toward native and out-of-the-box. If I want to normalize some data, I don’t want to write a custom Python script from scratch. I want to have a normalization node, block, action, where I say that all dates must be in a yyyyMMdd format. Otherwise, there’s no point in using the tool over using straight Python.
As expected, established solutions from before the times of the LLM popularity score much higher on the integrability axis. This means that these tools have more mature capabilities for integrating with your existing tech stack. Integrability is also a set of capabilities which takes very long to develop.
Writing integrations, engaging with technology partners, building a community, and offering out-of-the-box content are very long exercises. It is not an apples-for-apples comparison to look at a 2024-established Stack-AI against a 2013-established Workato in terms of the integrability featur