As business leaders look for advantages in the race to integrate AI agents into their operations, no- and low-code building tools offer an alluring pathway to internal adoption. “No-code agent builder tools accelerate the creation of AI agents by citizen developers, and tech leaders must establish clear definitions and scope their use,” Gartner Analyst Jason Wong said in a LinkedIn post announcing their minting of a new term for the era of no-code AI development: No-Code Agent Builders (NCAB). [1]
As Figure 1 shows, the emergence of AI agents and no-code building tools has created a new role in NCAB, which turns anyone with an idea about how to automate something into a developer. By democratizing the act of software creation, no- and low-code tools empower employees on the micro level while changing an organization’s relationship with SaaS on the macro level.
Figure 1: NCAB at the Overlap of No-Code Tools and AI agents
This article explores the benefits of no- and low-code development tools offered by Agent Platforms, as well as some of their limitations and drawbacks. Along with low-code AI platforms, a new class of building tool, the Agent Platform, is also explored. There are now ways that no-code can be combined with an open and flexible technology framework to give organizations control over their own software creation.
The Power of Democratized Development
One of the obvious advantages of no-code AI development is that it empowers non-developers to build useful software solutions. Traditional programming skills are no longer required to participate in software development. This empowerment comes from:
- Visual Interfaces: These tools use drag-and-drop programming, allowing users to design and implement solutions visually. This approach reduces the complexity associated with traditional coding, making it accessible to a broader audience.
- Rapid Prototyping and Iteration: Non-developers can quickly create and test solutions, fostering a culture of experimentation and innovation. This rapid development cycle is crucial for organizations aiming to maintain agility in fast-paced environments.
- Lower Barriers to Entry: By allowing non-technical staff to design AI-driven processes, these tools transform them from passive consumers into active creators, fostering a culture of innovation and resilience within organizations.
Provided the proper framework is in place, a long-term benefit of democratizing AI is organizational agility. Along with no- and low-code building tools, organizational agility also demands:
- Flexible and Open Ecosystems: Rather than relying on rigid, single-vendor solutions, open systems can integrate diverse technologies. This flexibility allows for quick adaptation and iterative improvements, essential for maintaining organizational agility.
- Accelerated Development and Deployment: The ability to design, test, and deploy solutions quickly reduces the time between conception and execution. This allows organizations to respond swiftly to changes, ensuring they stay competitive.
- An Empowered Workforce: By equipping everyone within an organization with the tools to contribute to software development, companies can harness the collective expertise of their workforce, leading to more effective and timely solutions.
Low-code and no-code platforms empower non-developers by simplifying the software creation process. Applied within the right technology ecosystem, this enhances organizational agility through rapid development, flexible integration, and a democratized approach to innovation.
Want to know more about how agentic AI is reshaping work?
Read WhitepaperCommon Challenges with No-Code and Low-Code Tools
While no- and low-code building tools can rapidly accelerate an organizations efforts with Agentic AI, many of the tools in the marketplace that offer ease of creation suffer from shortcomings that make it difficult to create a truly agentic system. Common challenges include:
- Limited Flexibility and Complexity Handling: No- and low-code tools often restrict the ability to implement complex logic, advanced features, or highly specialized workflows. Users may be unable to go beyond the templates and modules provided, which can be a significant limitation for special or more sophisticated use cases.
- Scalability Issues: As projects grow more complex or attract more users, no- and low-code solutions may struggle to scale effectively. Performance bottlenecks can occur, and the platforms may not handle high-volume or real-time data processing as efficiently as fully coded solutions.
- Poor Design Quality: Non-experts might create suboptimal designs without proper governance and training. No- and low-code tools make it easier to create AI agents, but they don’t make it easier to know what kind of AI agents to make.
- Vendor Lock-In: Some no- or low-code platforms are constrained by a limited set of tools and the inability to incorporate outside systems easily. Single vendor platforms can limit flexibility as needs evolve, making it impossible to develop organizational agility.
- Maintenance Challenges: Where enterprise AI tools are concerned, ensuring compatibility and security may require deeper technical oversight than non-technical users can provide.
The bottom line is that the simplicity on the front end doesn’t reduce complexity on the backend. Allowing anyone inside an org to try their hand at software development can have massive benefits, but it still requires teams who understand the technical complexities presented on the back end. In essence, agentic systems exist to manage complexity and there need to be people involved in building one that understand the scope and depth of the complexity teeming within most organizations. To that end building an agentic system requires a platform that is adaptable enough to grow over time and open so that it can incorporate existing software.
Agent Platforms Provide Essential Tools and a Proper Framework
As an emerging class of technology, Agent Platforms offer organizations the opportunity to make rapid progress building AI agents using no- and low-code tools along with the framework for scaling up to multi-agent systems. Important characteristics and functions of an Agent Platform include:
Autonomous AI Agents
Develop and deploy autonomous agents that can work independently, executing tasks and workflows with minimal user interaction.
Low-Code/No-Code Setup
Build, edit, and deploy agents quickly without a lot of coding, allowing for agility and quick responses to business requirements.
Tool and API Integration
Connect effortlessly with a broad selection of systems (CRM, email, databases, calendars, etc.) via native integrations and adaptable APIs.
Context Retention and Memory
Agents save context from past conversations, allowing for better decision-making and state continuity across tasks.
Modular Task Orchestration
Dissect complex processes into smaller steps with agents working together or passing on tasks, as required.
Multi-agent Orchestration
Facilitation of chains or swarms of agents collaborating on various segments of a workflow in pursuit of organizational objectives.
Human-in-the-Loop Functionality
Ascend difficult or uncertain cases to human agents to maintain control and make precise decisions.
Ongoing Learning
Agents learn and develop over time through learning from data and experience, optimizing their performance and responding to evolving needs.
Comprehensive Analytics and Reporting
Monitor agent performance, track results, and create actionable insights with robust reporting tools.
Impose role-based access, context-specific controls, and configurable security to safeguard sensitive data and maintain compliance.
Outcome-Driven Automation
Automate for business outcomes and not just isolate tasks, orchestrating complete workflows for strategic value.
Using Agent Platforms, anyone with an idea about automation can quickly deploy and test potential solutions. Agent Platforms, such as OneReach.ai’s Generative Studio X (GSX), enable groups to accelerate the early stages of agent development while laying a foundation for growth that allows scaling both horizontally (adding more instances as demand increases) and vertically (upgrading resources for agents using more CPU).
As shown in Figure 2, building individual agents can be as simple as giving one a name and a basic description. Figure 3 shows what an AI agent created using prompts and generative tools.
Figure 2: Agent Builder Tool in the GSX Platform
Figure 3: Agent Fine-Tuning with the GSX Platform
At this stage, the Agent Platform allows users to adjust the objective and begin adding actions, connecting knowledge bases, and select large language models (LLMs) before connecting the agent to a workflow, as shown in Figure 4.
Figure 4: Agent Flows in GSX
An Agent Platform of this scope allows creation of AI agents, integration into flows, connection with other AI agents, deployment into test environments, and quick iteration of viable AI agents to production. For the tech leaders within an organization, Agent Platforms give them control over security and governance along with the ability to orchestrate multiple AI agents to complete increasingly complex objectives. Agentic Platforms can also create ecosystems where modular components of AI agents — ”skills” they are often called — can be modified and used by multiple agents organization-wide.
The ability to customize skills from a shared library means Agent Platforms can enable faster building of agents and with increased sophistication. As new skills are created and added to the shared library, the opportunities to create agentic solutions increase sharply.
Write Your Own Software, Control Your Own Destiny
Often lost in the hype surrounding AI agents is the fact that organizations can use agentic systems to create their own software. This goes beyond just writing new software. As agentic systems are trained to complete process automations that involve legacy SaaS systems, they move into position to be able to recreate the necessary components of those systems. Because they are conversational in nature and can interact through a rich web chat (RWC) window, the SaaS system’s UI is no longer relevant. Things like business logic, spreadsheets, data processing, and cloud storage can also be generated in-house.
This is what European fintech giant Klarna set out to do last year, when it announced it was shutting down its software as a service (SaaS) providers Salesforce and Workday. Their goal was to create an agentic system with a “number of large internal initiatives that combine AI, standardization, and simplification to enable us to shut down several software-as-a-service providers.” [2]
When applied properly and in combination with the correct framework, no- and low-code tools for building AI agents can boost an organization’s efforts where AI agents are concerned. More importantly, they can accelerate the process of creating a technology ecosystem where multiple AI agents are sharing information and doing real work on behalf of users.
OneReach.ai’s GSX Agent Platform allows organizations to automate tasks, workflows and processes. It empowers organizations to undertake Agent Lifecycle Management: starting from design, training, and testing to deployment, monitoring, and optimization of intelligent multimodal agents.
