Let's Understand How to Build Efficient Agentic Systems!

The advent of artificial intelligence has paved the way for intelligent systems capable of handling complex tasks. From simple chatbots to advanced agentic systems, the evolution of AI applications has been remarkable. As businesses and developers aim to create systems that are both adaptive and autonomous, agentic systems have emerged as a powerful paradigm. This week's newsletter edition delves into the foundational aspects of building efficient agentic systems and applications, exploring their workflow, core components, techniques, the evolution of AI systems, and much more.

Introduction to AI Agents

AI agents are autonomous systems designed to perceive their environment, analyze information, and execute actions to achieve specific objectives. Unlike traditional AI systems, which primarily focus on responding to user inputs, AI agents incorporate planning, reasoning, and decision-making capabilities. They are equipped to handle multi-step processes, interact with APIs or external databases, and continuously improve through feedback loops.

For instance, while a chatbot might answer a query about weather, an AI agent can plan an entire itinerary based on weather conditions, user preferences, and available resources. This autonomy and capability to execute complex workflows distinguish AI agents from simpler AI systems.

These agents, by integrating with various tools and data sources, can operate autonomously to solve complex problems, making them invaluable in a rapidly evolving technological landscape. Agents are empowered with tooling to go ahead and take the action and create a workflow for complex problems/queries.

Let's see an example below,

Human: Which company did the inventor of the telephone start?

Following is a sample of thinking steps that an agent may take.

Agent (THINKING):

→ Thought: I need to search for the inventor of the telephone.

→ Action: Search [inventor of telephone]

→ Observation: Alexander Graham Bell

→ Thought: I need to search for a company that was founded by Alexander Graham Bell

→ Action: Search [company founded by Alexander Graham Bell]

→ Observation: Alexander Graham Bell co-founded the American Telephone and Telegraph Company (AT&T) in 1885

→ Thought: I have found the answer. I will return.

→ Agent (RESPONSE): Alexander Graham Bell co-founded AT&T in 1885

You can see that the agent follows a methodical way of breaking down the problem into subproblems that can be solved by taking specific Actions.

Frameworks like LangChain and LLaMAIndex give you an easy way to build these agents and connect to toolings and API.

Evolution of Agentic Systems

The evolution of AI systems can be categorized into three stages:

AI Chatbots

AI chatbots represent the foundational layer of intelligent systems. These systems follow a linear flow: user input is processed by a language model to generate a response. While they are effective for simple query processing and conversational tasks, they are limited in scope and lack autonomy. Chatbots are reactive by nature, addressing user queries without the ability to plan or execute multi-step tasks.

AI Agents

AI agents mark the next stage of evolution. These systems incorporate task analysis, planning, and execution capabilities. Upon receiving user instructions, they analyze the task, devise a strategy, and execute actions, often interacting with APIs or external data sources. Feedback loops allow them to refine their approach, making them adaptive and capable of handling more complex workflows. For example, an AI agent might automate the process of managing a marketing campaign, from strategy creation to performance analysis.

Agentic Systems

At the pinnacle of AI evolution are agentic systems, which involve the orchestration of multiple specialized agents under a central coordinator. Here, a user’s goal is decomposed into sub-tasks, each handled by dedicated agents. The coordinator ensures seamless integration of individual outputs into an aggregated result. This structure allows agentic systems to tackle multifaceted problems requiring diverse expertise, such as coordinating supply chain logistics or managing a multi-step product development process.

Core Components of AI Agents

Efficient agentic applications rely on several core components:

1. Perception Module: Responsible for gathering and processing sensory or input data. This is the foundation for understanding the environment or task requirements.
2. Reasoning Engine: Facilitates logical decision-making and strategy development. This component determines the agent’s next steps based on the input and context.
3. Memory System: Stores and retrieves contextual data, enabling the agent to recall previous interactions and apply them to current tasks. Episodic and semantic memory systems are often employed.
4. Action Generator: Executes decisions by interacting with external systems, APIs, or performing specific tasks within the environment.

Workflow of AI Agents

The workflow of AI agents follows a structured five-step process:

1. Observation: The agent gathers and processes input data from various sources.
2. Analysis: It contextualizes the input to derive meaningful insights.
3. Planning: The agent formulates a strategy to achieve the desired objective.
4. Decision: Based on the analysis and planning, it selects the most appropriate course of action.
5. Execution: The agent implements the decision and monitors the outcome.

This workflow is supported by short-term feedback loops, which refine immediate actions, and long-term learning loops, which improve the agent’s overall performance over time.

Techniques for Building AI Agents

Developing efficient AI agents requires a combination of advanced techniques and best practices:

1. Prompt Engineering: Crafting clear and concise instructions for the agent to optimize performance. Techniques such as context optimization and explicit instruction design are key.
2. Tool Integration: Enabling the agent to interact with APIs and external resources for task execution. Effective orchestration ensures smooth workflows.
3. Reasoning Patterns: Employing techniques like chain-of-thought or tree-of-thought reasoning to guide logical decision-making.
4. Memory Systems: Utilizing vector databases to store episodic and semantic memories, enabling context recall and improved adaptability.
5. Safety Mechanisms: Incorporating validation layers and guardrails to ensure ethical and reliable behavior.
6. Evaluation Metrics: Defining performance KPIs and quality benchmarks to assess and refine the agent’s output.

Building Efficient Agentic Applications

Combining these components and techniques leads to the creation of efficient agentic applications. The process begins with understanding the user’s goals and designing agents with specific roles. Feedback loops play a crucial role in refining workflows and ensuring continuous improvement. Integrating robust memory systems, reasoning engines, and safety mechanisms further enhances reliability and performance.

For example, an agentic system designed for supply chain management might include:

• Agents for demand forecasting, inventory tracking, and logistics planning.
• A coordinator to integrate outputs from these agents into a cohesive strategy.
• Evaluation metrics to monitor performance and suggest improvements.

Building Efficient Agentic & LLM Applications

As you may already know, I talk a lot about AI agents and agentic systems, I keep exploring tools/platforms and approaches to build efficient LLM and agentic applications. I am a huge fan of LangGraph lately because it helps you build agentic workflows easily. Similarly, during my search for finding better platform to build agentic and LLM-powered applications, I found Arato, an end-to-end platform for delivering production grade AI applications.

You can build AI agents using Arato.ai’s platform. The platform is designed as an end-to-end solution for developing generative AI applications. Its core components—such as the AI Notebook, which lets you design, iterate, and experiment with prompt sequences; evaluation modules for testing and fine-tuning; and DataHub for managing dynamic datasets—provide you with the building blocks necessary to create complex, autonomous AI workflows. These capabilities allow you to chain multiple prompts and models together, effectively enabling the development of AI agents that can perform specific tasks or even operate autonomously in production environments.

In essence, while Arato.ai is a comprehensive generative AI development platform, its flexible architecture and integrated toolset make it well-suited for constructing AI agents tailored to your specific business needs.

I tested their sample travel planner project in minutes and really impressed with the results and what they have built at Arato.

I started playing with the platform and created some more custom projects. I think Arato is a highly recommended platform for anybody building AI applications. I want you to try Arato and let me know what you guys think.

Understanding Different LLM Frameworks

One crucial step while building a robust AI/LLM application is evaluating which framework aligns best with your goals. To make it easy for you, I have a comparison table of the well-known LLM frameworks and this should help you pick the best one that suits your requirements.

LangChain remains a go-to for its rich ecosystem and powerful multi-step orchestration, making it excellent for building complex chatbots and dynamic workflows—though its steep learning curve and rapid updates require careful version management.

In contrast, LlamaIndex (GPT Index) is optimized for retrieval-augmented generation, offering flexible indexing that’s perfect for document Q&A and knowledge retrieval, even though managing large datasets might demand additional resources.

CrewAI shines with its straightforward role-based agent approach and quick setup, ideal for routine operations and data analysis, despite its smaller community support and limited third-party integrations.

Meanwhile, AG emphasizes built-in multi-agent collaboration and human-in-the-loop capabilities, positioning it well for enterprise automation and large, multi-step processes, though its higher compute overhead could be a factor.

Swarm provides an API-first, lightweight design with minimal setup, making it particularly attractive for rapid prototyping and stateless applications, despite lacking advanced orchestration features.

Adding further depth, Haystack is recognized for its robust search and retrieval pipeline—an ideal solution for enterprise search and document Q&A—while Semantic Kernel by Microsoft offers a lightweight, modular .NET SDK that excels in prompt chaining and memory management, best suited for enterprise app integration within the Microsoft ecosystem.

Collectively, these pointers allow developers to align each framework’s strengths with specific use cases, ensuring a balanced approach between complexity, scalability, and ease of deployment.

Understand Agentic RAG Using CrewAI & LangChain.

Understand how to build RAG application using Haystack.

The transition from chatbots to agentic systems underscores the growing sophistication of AI applications. By leveraging core components, structured workflows, and advanced techniques, developers can build efficient agentic applications that are both autonomous and adaptive. These systems have the potential to revolutionize industries, enabling smarter decision-making and streamlined operations. As AI continues to evolve, agentic applications will remain at the forefront, driving innovation and solving complex, real-world challenges.

Recently, I did a LinkedIn live session with Madhukar Kumar, the chief technology evangelist and CMO at SingleStore on the building agentic RAG systems, you can watch it in the below YT video.

Also, always choose a robust database to build your AI systems. I recommend exploring SingleStore as your all-in-one database.

Try SingleStore for free!

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