No Code AI Agent Builder

AI is no longer confined to research labs or big tech companies: It’s gradually becoming an integral part of how businesses operate. However, the biggest barrier to leveraging AI in enterprises is the technical expertise required for implementing, fine-tuning models, and integrating proprietary data. 

No-code AI agent builders lower the barrier to adoption. They provide a visual drag-and-drop interface, along with several pre-built logical blocks, to enable business analysts, instead of developers, to build AI agents. These agent builders democratize AI adoption and remove dependency on the precious few AI engineering resources. 

This article focuses on the key aspects of no-code AI agent builders, including the must-have features and the key technologies available in the segment. 

Summary of key no-code AI agent builder concepts

Understanding no-code AI agent builders

No-code AI agent builders are platforms for implementing agents through a visual interface with built-in plugins for data integration and access to external tools. Building an AI agent involves stitching together many complex modules in a complex sequence.

At a high level, an AI agent has the following components:

• LLMs: These models are used for processing input and decision-making.
• Knowledge bases: The KBs are primarily vector databases that help store and retrieve data according to input. The retrieved data is fed as context to LLMs for decision-making.
• Input parser: This module makes sense of user input or the trigger input and formats it for LLM processing. 
• Output parser: A block that helps format LLM responses into a format suitable for the user, a downstream API, or another agent. 
• Contextual memory: Long- and short-term memory to ensure that responses do not lose context.

• Tool executors: APIs or functions that interact with the external world.

No-code AI agent builders have built-in functions to integrate with your choices of LLMs, vector databases, contextual memory, and tools. Having such a platform with a library of all the building blocks required to implement an agent is very beneficial for modern organizations for several reasons: 

• Democratized AI agent development: With several moving parts, implementing an AI agent and optimizing it requires deep engineering expertise. With LLMs and agents being a nascent phenomenon, engineering teams often have to go through a steep learning curve before building production-grade agents. No-code AI agent builders help cover this learning curve by enabling anyone with a basic idea to build agents. 
• Faster prototyping: No-code AI agent builders can help teams build prototypes very quickly since they already have the common design patterns built in. They come with preconfigured models for reasoning and embedding. They also come with workflow patterns that can be quickly translated to your organization’s perspective. No-code AI agent builders also support integration with common applications used within an organization, like CRMs, ERP, etc. 
• Cost-effectiveness: No-code AI agent builders help accomplish more with smaller engineering teams than is possible with custom development. They do not require custom model deployment and, in most cases, infrastructure cost is baked into a single subscription price. 

Having now explored the benefits of no-code AI agent builder frameworks, let’s discuss the core features that one must look out for while selecting one.

Visual drag-and-drop interface

The user interface of the framework is critical to ensuring the productivity of agent building teams. The framework that you select must have the ability to drag and drop all the basic components of an agent and connect them to implement dynamic flows. The components must be represented as nodes, and the connections define how information flows among different nodes. The platform must allow the building of agents, just like defining a flow chart. 

Comprehensive memory node configuration

Memory is what makes agents stateful. The platform must support short-term memory, long-term memory, and knowledge bases. Short-term memory is used for storing task-specific information, while long-term memory is used to store conversational history and user profile information. Knowledge bases are used to provide high-volume organizational knowledge as context for the LLM.

Support for API and third-party applications integration

At a high level, the platform must support database integration, CRUD operations, CSV or spreadsheet lookups, and API calls using REST, GraphQL, and other protocols. This is what helps agents extend beyond simple chat programs and become autonomous problem solvers. Agents must be able to perform actions as opposed to just responding via queries. 

The platform must also support integrations to popular ERPs and CRM tools. Webhooks are another aspect of agent integration; they help define endpoints that get invoked when a specific event happens.

Comprehensive flow pattern library

While implementing agents, the ability to instantiate popular workflow patterns in a predefined manner can save a lot of time and effort. Agentic workflows can range from simple linear or sequential workflows to conditional flows and event-triggered flows:

• Sequential workflows: These flows are helpful while implementing simple use cases like tutorials or basic customer support. For example, a flow where one needs to intercept a chat message in Slack with a specific tag and respond back with an LLM-generated output is a sequential flow.
• Conditional flows: Conditional flows include if-else statements that enable agents to branch to different flows based on use cases. For example, a customer support agent may route the request to order tracking or product search flows based on what the customer is asking. Conditional flows are also used when the execution logic has to run continuously until a specific condition is met. 
• Event-based triggers: Triggers are used when the agents need to start execution based on a pattern of events. For example, an agent may need to create an onboarding document tailored to a customer whenever a new entry is made in the CRM portal.
• Data-driven flows: Agents often need to access integrated relational databases or data APIs provided by third-party applications. For example, an agent may need to find out the segment of a specific customer when it needs to create an onboarding document specifically for that customer. 

Support for multi-agent orchestration

Agents have become complicated software systems on their own lately and continue to become more complex. A “super agent” that does everything is considered a bad pattern because of a lack of modularity, fault isolation, and difficulty in troubleshooting; it’s better to have multiples. For example, a customer support agent can be divided into multiple agents that handle product search, order tracking, and FAQs. Your no-code AI agent platform must support multi-agent orchestration through drag-and-drop interfaces.

Human-in-the-loop workflow

While agents are now more intelligent than ever, outsourcing critical workflows to agents alone is still a big risk. Most organizations prefer a human-in-the-loop workflow where the agent does the heavy lifting but the eventual output is finally verified by a human and approved to be used in the system. For example, in the earlier custom onboarding document preparation, the agent may prepare a document, but it will be sent to the customer only after the person responsible approves it. No-code AI agent platforms must support human-in-the-loop workflows natively. 

Output structuring

AI agents often need to collaborate with other systems in the form of integrations. To make this possible, the LLM responses need to be formatted to standard structures like JSON, XML, or other custom schemas. The no-code AI agent platform needs to be able to format agent responses to suit a standard structure. 

Support for simulations and tests

Taking an AI agent to production requires comprehensive testing, ranging from basic business logic verification to checking for hallucination, bias, toxicity, etc. The AI agent platform must have the ability to run automated tests with simulated inputs. Good agent platforms have features to auto-generate data based on defined agentic workflows and test it. Such simulated tests help identify weaknesses in prompts, debug workflows, and let you observe how real users will interact with your system. 

No code AI agent builder tools

The realm of no-code AI agent builders has expanded quite a bit in the last year. Let’s explore some of the more popular tools in this domain.

Azure Copilot Studio

Azure Copilot Studio shines in the enterprise AI agent builder space because of its tight integration with Azure services like SharePoint, Azure Files, Azure AD, etc. It is the default choice for enterprises that need a quick solution to enable AI for their workforces. It has native access to Azure Open AI models and can access data from within Microsoft Dynamics 365 and SharePoint. 

However, while Azure Copilot Studio provides basic options to manipulate prompt and LLM configurations, it falls short in the complex data transformation space. Use cases that need complex data manipulation before and after feeding information to LLMs will require using Azure’s other services, like Data Factory or Synapse Analytics. In such cases, the advantages of a no-code agent builder are lost. 

Langflow

Langflow provides a visual interface to build and test agents; note that while it is tightly integrated with Langchain, these are two distinct product offerings. LangFlow shines in its ability to quickly prototype AI agents (compared to LangChain) but suffers from less polished integration with enterprise connectors when compared to offerings like Copilot Studio. Langflow has fewer built-in templates and is often considered less beginner-friendly. 

N8N

N8N boasts a comprehensive integration ecosystem with most of the popular CRMs and ERPs supported. It is much more than a chatbot system simply because of its integration with enterprise applications. It goes one step beyond Copilot Studio in this regard since its integration capabilities span well beyond the Microsoft ecosystem.

N8N is open source and can be self-hosted if required. It does fall short in data manipulation capabilities, especially when it comes to complex data patterns. 

Open AI Agent Builder

Open AI agent builder provides a no-code way to integrate Open AI models with memory, APIs, tools, and databases. For organizations that are fully committed to the OpenAI ecosystem, this is a great alternative. It is cheaper to use than Copilot Studio and much more flexible. That said, it is much less enterprise-ready than Copilot Studio because it lacks the built-in governance and compliance assurances that Microsoft can provide. Like all the other alternatives mentioned here, the OpenAI agent builder also suffers from a lack of ability in complex data manipulation. 

FME by Safe Software

Safe’s Feature Manipulation Engine (FME) is a no-code AI agent builder optimized for data-heavy workflows. It was originally a spatial ETL tool and has since gained many features related to building AI agents. Its roots enable it to be equally effective at complex data manipulation and no-code AI agent implementations. 

FME is a powerful tool for organizations managing spatial computing data and complex data transformations. Let’s explore how to use FME to create a workflow.

Consider a workflow where an AI agent is used to analyze the sentiments of customer reviews. Let’s assume the customer reviews are stored in a file called review_dataset.csv. The agent needs to process the non-empty rows in this dataset one by one, find the sentiment using the OpenAI LLM, and then insert the actual review and its sentiment to DynamoDB, a popular NoSQL database used to store unstructured data. 

Let’s see how it is done in FME.

1. Initialize an input reader in FME and point it to your CSV file.

2. Initialize an attribute filter in  FME. The attribute filter helps filter out all rows that are empty based on specific field names. The attribute filter will have a missing value filter by default; all you need to do is select the field by which you need to filter. Connect the CSV file reader to the attribute filter, and it will show the fields in your CSV as options for filtering. Here, we select “review” as the field that we need to validate against non-empty values.

3. Initialize the OpenAIChatGPTConnector from the FME transformer and connect the “unfiltered” port of the attribute filter. You can configure your API key, system prompt, and user prompt in the configuration window. We can use a simple prompt to instruct the LLM to classify every review as positive, negative, or neutral.

4. Initialize an output writer using the built-in DynamoDB connector in FME. You can configure your AWS Secret Key ID and secret key in the configuration window.

5. Connect the output of the ChatGPT connector to the DynamoDB writer.  Your complete agent workflow will now look as follows.

That’s it! Building agents in FME is as easy as dragging and dropping a few components, configuring them, and connecting them to create a flow. FME makes it very easy to create data manipulation agents like this. 

Best practices for no-code AI agent platforms

No-code AI agent builders have reduced the barriers to entry in the AI agent space and enabled organizations to quickly build agents without a large engineering workforce. That said, having such a powerful tool at one’s disposal often leads to chaotic, unmanageable flows that can quickly get out of hand. Following best practices helps organizations create high-quality, reliable flows using no-code AI agent builders.

Start with clear use case boundaries

A clear definition of use cases and the methods to test the effectiveness of the use case implementation goes a long way toward ensuring a successful outcome. The use case must be defined in terms of user stories and expected outcomes with clear boundaries. This definition will also help with creating test cases for ensuring the quality of implementation. 

With Gen AI being a very valuable tool now in testing, a comprehensive use case definition can be directly fed to LLMs to generate test cases, scenarios, and even test data. For example, consider that you are implementing an agent to parse emails and make an API call based on their contents. The use case definitions must include the type of content and the exact attributes that will be extracted from the email. It should also include failure scenarios where the agent can reject emails. 

Design data flows before building

Agents are only as good as the data that is fed as context to them. Most AI agents require data to be transformed into a form that LLMs can make sense of. Hence, it is important to define the data flows and implement them before building the AI agent flows. 

For example, consider the email parsing agent referred to in the previous example. To make a decision about an email, it may need to refer to the organization’s customers and the last order that was delivered to them. This information will have to be fetched from an API, transformed to a readable format, and passed to the LLM along with the email to be parsed. Using an AI agent framework like FME that has support for complex data transformations is a good idea in such use cases. 

Use modular blocks instead of long chains

With the ability to quickly drag and drop flows, it is very easy to lose sight of the eventual debugging and maintenance procedures that the agent will go through once it is in production. It is important to use modular flows as opposed to giant chains that do everything within a super agent. 

One must also consider multi-agent architectures with a clear division of responsibility to ensure ease of debugging and maintenance. For example, in the example of the email parsing agent that we referred to earlier, it may be a good idea to create one modular agent that parses emails related to order delivery concerns and another to address product detail queries.

Constrain the LLM

LLMs can hallucinate if not clearly instructed about their tasks. The design of AI agents must consider ways of constraining the LLMs with clear guardrails. Leaving LLMs to provide an open-ended response is a clear formula for failure, so one must be careful to always provide a set of options to choose from wherever possible. Guardrails for detecting bias and toxicity are very important in the case of agents that handle human-generated data or are part of critical organizational decisions. 

Version everything

No-code AI agent builders make it very easy to build agents, so it is very common to expose them as self-service systems for employees, leading to several versions of agentic flows that essentially do the same thing. Keeping versions of everything and ensuring a collaborative environment where employees reuse flows built by their peers is very important when adopting such frameworks in enterprises. 

Use humans in the loop

While it is tempting to let the agent do everything on its own, this is seldom advised in enterprises where decisions have huge consequences. One must always use a human-in-the-loop paradigm where the agent does the heavy lifting and then routes its outputs for approval by a human being. 

That said, this may not always be practical in the case of high-frequency workflows (like chatbots) and event-based ones. In such cases, one must define a threshold value for each event that is processed to represent the confidence of the agent in its actions, and actions not meeting thresholds can be directed for human approval. This can be done with a little bit of prompt adjustment. For example, in the email parsing agent that we referred to earlier, it may be worthwhile to add a human-in-the-loop workflow where the agent thinks its confidence in addressing an order-related concern is low. The prompt can be written so that the LLM responds with a confidence value along with its response, and responses with lower confidence can be pushed to a human for review. 

Use simulated tests

One advantage of building agents in a modular way is that they can be tested independently. Using automated tests to iteratively test your agent is a must before rolling it out to production. 

Good AI agent platforms can auto-generate code and data to test workflows based on use case definitions. If agents are built with modularity in mind, tests can be defined for each of the smaller modules. The complete agent can then be tested with a set of regression and integration tests. 

Consider observability

Logging prompts, user inputs, decisions taken by the agent, and the output from tools and APIs is critical for troubleshooting problems. This information can also be used to define human escalation thresholds and refine prompts for better outputs. 

This is also a good way to measure user dissatisfaction. Your no-code AI agent tool must support logging without much manual effort and must be capable of exporting it for deeper analysis.  

Last thoughts

Building AI agents has transformed the way enterprises build agents. These platforms can help organizations move from prototype to production without using large engineering teams and lengthy ML ops pipelines. 

Tools like Langflow, OpenAI agent builder, etc. can help in quick experimentation. Copilot Studio is a great alternative when you have agent workflows tightly integrated with the Microsoft ecosystem. N8N is a great alternative for its flexibility and comprehensive integration. However, most of the tools available today fall short when it comes to complex data transformation, ETL processes ,and granular datasets like geospatial data. FME is a good alternative to consider for such use cases. It can help in a wide variety of applications, including working with GIS layers, cleaning IoT sensor feeds, and preparing enterprise datasets for downstream AI tasks.