Low Code No Code AI Agents
We are in the era of AI Agents, as such the demand for faster development to deployment of AI Agents is the need of the hour and this has resulted in an unprecedent increase in the need for citizen developers to step up their game. Forrester estimates the combined low-code and digital process automation (DPA) market, which was valued at $13.2 billion at the end of 2023, could reach $50 billion by 2028 if fuelled by the growth of AI (a 33% annual growth rate). 93% of developers spend at least some of their time using low-code tools. Enterprises are adopting low-code solutions for their flexibility (83%), speed (63%), and process automation capabilities (67%). The technology frees up professional developers from routine tasks, allowing them to focus on more complex, strategic projects.
In this blog, we will be exploring the below concepts:
- Traditional AI vs Low Code No Code (LCNC) AI approach for building & training AI
- Agentic AI vs Chatbots
- Core building blocks for an Agent
- Agent Design Framework
- Types of Agentic AI & Reasoning approaches in AI
- LCNC External Tools
Traditional AI vs No Code AI
Image 1 : Traditional AI training & deployment approach
Image 2 : LCNC training & deployment approach
The differences between the two are illustrated in the table as below:
Feature | Code-based approach | Low Code No-code approach |
Speed to Prototype | Slower due to coding, debugging, and testing | Faster with visual tools and pre-built components |
Ease of Use | Requires programming skills, steep learning curve | Accessible to non-technical users, low learning curve |
Custom Logic & Control | High customization, full control over logic | Limited customization, relies on pre-built components |
API Integration | Flexible, integrates with any API | Pre-built connectors for popular APIs, less flexible |
Scalability | Highly scalable, handles large datasets and complex tasks | Limited scalability, depends on platform capabilities |
AI Agent Complexity | Supports complex, autonomous agents | Best for simpler, static workflows |
Teamwork | Requires technical expertise, supports version control | Enables collaboration with non-technical users via visual interfaces |
Debug and maintain | Challenging for complex systems, robust debugging tools available | Easier for simple workflows, complex issues may need platform support |
Data Privacy | Greater control, requires proper implementation | Relies on platform’s security, some offer strong compliance |
Cost | Higher initial costs, cost-effective for large-scale projects | Subscription-based, cost-effective for small to medium projects |
Agentic AI vs Chat Bot
An AI Agent is one which is capable of Thinking. It’s not a static rule-based automation system, it can comprehend its environment and its parameters and variable, reason and come to a decision.
Take action to accomplish a goal.
Function without continual human input
Chatbots on the other hand often lack the ability to reason and are not equipped to learn and process information to act on the go.
Takeaway: AI agents don’t just follow instructions – they adapt, reason, and make decisions based on context rather than rigid rules, in contrast to classical automation or is limited to text/image generation.
Three core building blocks of an Agentic AI system are as follows:
- Reasoning and Planning divides large objectives into manageable steps considers multiple steps rather than a single shot and modifies the plan considering fresh information
- Utilizing Tools to accomplish the listed tasks to achieve the said goal. Additionally, AI Agents intelligence is also the ability to select the appropriate tool at the appropriate time.
- Recollection Short-term memory: context of tasks and conversations Long-term memory: gains knowledge from previous exchanges gradually becomes better rather than making the same mistakes.
Image 3 : Three core blocks of an AI Agent
Agent Design Frameworks = Methods LLMs Use to Think, Act & Work Together.
Agentic AI is created by constructing one or more LLMs to collaborate effectively. This area is developing — and your designs can transform into frameworks as well.
Fundamental Agent Design Patterns are as below:
- Reflection - "How was my performance?" - The agent assesses its own results and enhances upcoming replies.
- Utilization of Tools - “What can assist me?” - Agents utilize resources such as web searches, APIs, databases, or code execution to accomplish tasks.
- Planning - “What is the most effective path ahead?” - The agent breaks tasks into step-by-step plans and executes them intelligently.
- Multi-Agent Collaboration - “Let’s ponder together.” - Numerous agents discuss, cooperate, or split tasks with collective or individual objectives.
The Core Idea
Agentic AI isn’t just about building smarter models:
It’s about creating smarter ways to think, act, and collaborate.
Types of Reasoning Approaches in Agent AI
Reasoning is the ability of the LLM to make decisions like a human in the absence of the human.
- Prompt-based reasoning - You write a detailed prompt asking an agent to build a marketing plan for a company, comparing it with the competition for the best possible approach that will work for this company. You could automate this as an agent AI task. The agent AI will execute this prompt and reason at every step to achieve the requested outcome and present a marketing plan.
- Reasoning with action (ReAct) - An agent AI tracks the safety of a manufacturing plant and makes decisions based on several inputs provided. It also takes input from the environment, which it incorporates into its reasoning to change its action plan. For example, there could be high moisture levels caused by a pipe leak. The agent may change its actions based on this data.
- Reasoning without observation (ReWOO) - An agent AI finds the right product for a customer to buy based on their behavioural data. It does not take any other input to change its decision.
Low-Code / No-Code Agentic AI Platforms
A low-code AI agent builder is a framework or platform that lets you define agent behaviour declaratively, rather than imperatively.
A low-code AI agent builder is a platform or framework that enables you to define agent behaviour declaratively rather than imperatively. Instead of writing extensive orchestration code, you simply describe what the agent should do.
- Goals
- Capabilities
- Tools
- Constraints
- Execution flow
The builder handles:
- Agent lifecycle
- Context propagation
- Tool execution
- Memory
- Retries
- Workflow coordination
We have described some of the tools as below:
Visual Builder Platforms
Platform | Description | Key Capabilities | Best Suited For | Pricing / Licensing |
Gumloop | No-code platform to visually create and deploy AI agents using a drag-and-drop canvas | • Intuitive flowchart-style UI | Business users building automation-first agents | Free plan available; paid plans start at $97/month |
Langflow | Python-based platform for building AI apps, focused on single-agent systems and RAG | • Open-source foundation | Developers & advanced users who want visual control with code extensibility | Open-source |
Flowise | Visual editor using LangChain-style abstractions for LLM and tool orchestration | • Flexible model integration | Teams building customizable LLM pipelines visually | Open-source |
Enterprise Platforms
Platform | Description | Key Capabilities | Best Suited For | Enterprise Strengths |
Microsoft Copilot Studio | Platform to build and deploy AI agents within the Microsoft ecosystem | • Deep Microsoft 365 integration | Enterprises already invested in Microsoft stack | Enterprise-grade security, governance, compliance |
Google Vertex AI Agent Builder | Google Cloud’s low-code platform for conversational agent creation | • Visual LLM workflow design | Cloud-native enterprises on GCP | Scalability, performance, cloud-native controls |
IBM AgentLab (Watsonx.ai) | Low-code drag-and-drop agent builder within IBM’s AI ecosystem | • RBAC, GDPR, HIPAA compliance | Regulated industries and large enterprises | Strong governance, compliance, enterprise controls |
Types of Agentic AI systems
Agent Type | Core Behaviour | Decision Basis | Environment Awareness | Adaptability | Example Use Case |
Simple Action Agent | Acts when a condition is met | Predefined rules | Minimal (current input only) | None | Sprinkler turns on when temperature exceeds threshold |
Model-Based Agent | Acts based on an internal model of the environment | Current state + internal model | Moderate (tracks environment state) | Limited | Vacuum cleaner detecting and collecting spills |
Goal-Oriented Agent | Chooses actions to reach a defined goal | Goal distance & outcome evaluation | High | High (adjusts actions dynamically) | Chatbot scheduling patient appointments |
Learning Agent | Learns from experience and self-improves | Feedback, reflection, performance evaluation | Very high | Continuous | Self-improving agent that refines decisions over time |
Utility-Based Agent | Optimizes multiple variables while reaching a goal | Utility function (cost, speed, efficiency) | Very high | High | Autonomous delivery robot optimizing route and energy |
Bluesky
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