Building Next-Gen AI Agents: Evolving Patterns and Best Practices

Generative AI (GenAI) is rapidly transforming industries, enabling businesses to create intelligent, self-learning systems. But moving from early-stage prototypes to full-scale production presents significant challenges. This blog series explores key patterns and best practices for building scalable, Agent-powered applications that thrive in real-world scenarios.

The Future of Agentic Development

We provide hands-on insights from building OmniSearch agents and an AI agent marketplace — where intelligent agents mimic department representatives, enabling seamless collaboration across teams. A major innovation is the “Agent-as-a-Service” model, a flexible, modular approach that helps businesses efficiently deploy, manage, and scale AI-powered agents.

Topics covered include direct prompting, embeddings, evaluations, Agentic retrieval-augmented generation, and fine-tuning. Whether you’re enhancing enterprise search or creating scalable AI-driven services, this series provides practical guidance for building next-gen agentic products.

Mastering Key LLM Techniques: A Visual Breakdown

Direct Prompting: The Simplest Way to Leverage LLMs

One of the easiest ways to interact with a Large Language Model (LLM) is via direct prompting — sending a query and getting an immediate response. This method requires no extra layers of logic, making it the fastest way to integrate AI into workflows.

For more on crafting highly optimized prompts, check out my blog on Prompt Engineering.

Example: AI Chatbot for E-Commerce

💬 User: “Where is my order?” 🤖 AI: “Please provide your order ID, and I can check the status for you!”

Business Use Case: This approach is ideal for customer support, content generation, and rapid AI prototyping.

In this setup, there are no additional layers of logic — just a direct exchange between the user and the LLM, making it a simple and easy-to-implement solution.

Another Example Prompt for Code Generation to drive business logic in Java:

Develop a Java method that accepts an array of integers. This method is designed to iterate over each integer in the array, square its value, and accumulate these squared values in a new array. Consequently, this new array, containing the squared values of the original elements, will be returned as the method’s output. For instance, given the input array {1, 2, 3, 4, 5}, the expected output should be {1, 4, 9, 16, 25}. The desired format for the output is a Java code snippet that accomplishes this task.

Evaluation (Evals): The New Way to Validate LLMs

Evaluate the LLM’s response according to the given task and its contextual relevance. When developing software, ensuring it performs as expected is crucial. Traditional systems achieve this through test cases, where specific inputs yield consistent outputs. If the system meets expectations, it passes the test.

However, LLM-based systems operate differently. They are non-deterministic, meaning the same input can generate varied responses. This variability makes conventional pass/fail testing ineffective. Instead, AI reliability is assessed using EVAL (Evaluation) metrics.

Software Testing vs EVAL

Unlike traditional testing, where exact answers matter, evaluating LLMs focuses on response quality and usefulness. This flexible approach ensures AI systems remain reliable and effective, even when their responses change.

Who Should Evaluate AI?

When evaluating LLMs, different techniques exist depending on who computes the score, leading to a key question: Who should act as the judge?

Evaluation Techniques

Automated Evaluation — Another LLM AI models or predefined metrics assess the responses.

• Examples: BLEU, ROUGE, GPTScore
• ✅ Fast & scalable
• ❌ May miss nuances like intent or context

Human Evaluation — People review and rate the model’s output based on accuracy, relevance, and fluency.

• Examples: Expert reviewers, user feedback
• ✅ Understands context & quality better
• ❌ Time-consuming & subjective

Self LLM Evaluation — Combines Self LLM AI metrics with human judgment for a balanced assessment.

• Examples: AI-generated scores reviewed by human moderators
• ✅ Efficient & context-aware
• ❌ Requires coordination & refinement

Ultimately, the best judge depends on the use case. While AI can provide quick assessments, humans remain crucial for understanding intent, ethics, and subtle nuances that automated scores may overlook.

Hands-On Example: AI Evaluation for OmniSearch Agents

Let’s test the OmniSearch AI agent handling an IT support query:

💬 User: “How do I reset my corporate email password?” 🤖 AI: “You can reset your corporate email password by visiting the IT self-service portal. If you need further help, contact IT support at support@company.com.”

Now, let’s validate the response using DeepEval:

from deepeval import assert_test
from deepeval.test_case import LLMTestCase
from deepeval.metrics import AnswerRelevancyMetric

def test_answer_relevancy():
    # Define the relevancy metric with a threshold
    answer_relevancy_metric = AnswerRelevancyMetric(threshold=0.5)

    # Create a test case for the IT Support Agent in the OmniSearch system
    test_case = LLMTestCase(
        input="How do I reset my corporate email password?",
        actual_output="You can reset your corporate email password by visiting the IT self-service portal and following the password recovery steps. If you need further assistance, contact IT support at support@company.com.",
        retrieval_context=[
            """To reset your corporate email password, visit the IT self-service portal and follow the password recovery steps.
               If you are unable to reset it, contact IT support at support@company.com for further assistance."""
        ]
    )

    # Run the assertion to evaluate relevancy
    assert_test(test_case, [answer_relevancy_metric])

# Execute the test
test_answer_relevancy()

Ensuring AI responses are relevant and accurate improves user trust and business adoption

Combining automated evaluation with human judgment is the gold standard for AI validation. While AI ensures speed and scalability, humans add context, ethics, and business alignment — making AI-powered systems more trustworthy and intelligent.

What’s Next? Upcoming Topics

In the next installments of this series, we’ll dive deeper into: ✅ Embeddings & Agentic Retrieval-Augmented Generation (RAG) — Enhancing AI’s ability to retrieve and synthesize relevant latest information. ✅ Fine-tuning — Customizing LLMs for domain-specific tasks and improved performance.

Stay tuned for practical insights and hands-on examples!

Interested in collaborating? Reach out to grve25@gmail.com

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