Low Code No Code Agentic AI systems and tools

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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:

1. Reasoning and Planning divides large objectives into manageable steps considers multiple steps rather than a single shot and modifies the plan considering fresh information
     
2. 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.
3. 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:

1. Reflection  - "How was my performance?" - The agent assesses its own results and enhances upcoming replies.
2. Utilization of Tools - “What can assist me?” - Agents utilize resources such as web searches, APIs, databases, or code execution to accomplish tasks.
3. Planning - “What is the most effective path ahead?” - The agent breaks tasks into step-by-step plans and executes them intelligently.
4. 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.

1. 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. 
2. 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.
3. 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.

1. Goals
2. Capabilities
3. Tools
4. Constraints
5. Execution flow

The builder handles:

1. Agent lifecycle
2. Context propagation
3. Tool execution
4. Memory
5. Retries
6. 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 • Strong workflow automation focus • Often described as “if Zapier and ChatGPT had a baby”	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 • Deep LangChain integration • Supports OpenAI, Hugging Face, TensorFlow • AI-centric workflows	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 • Custom tools & extensions • Open-source with active community • Strong LLM orchestration focus	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 • Customer query handling • Sales lead identification • “AI employees” concept	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 • Native Google Cloud integration • High scalability • Custom APIs & system instructions	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 • Advanced governance tools • Highly customizable agents	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

In conclusion, while the surge of Agentic AI tools offers exciting possibilities, adopting them isn’t just about jumping on the latest trend. Organizations must weigh the cost of implementation and ongoing maintenance, ensure data privacy and security, and carefully evaluate risks, ethical implications, and environmental impact. Thoughtful planning and responsible deployment will determine whether Agentic AI becomes a true enabler of innovation or an expensive experiment.