At the risk of sounding trite, AI agents comprises two concepts:

  1. Agents - They can complete an action on their own, with minimal/ zero human intervention.
  2. AI - They are powered to do this by AI models.

While AI tools have been present since early 2010s, AI Agents as a concept achieved an inflection in 2024. This is because there are a whole range of AI models & techniques that can be used to solve different types of problems.

State of AI

AI Agents have surged in popularity between 2024–2025 as advancements in specialized AI models enabled autonomous problem-solving across diverse domains. The proliferation of large language models (LLMs) and modular AI frameworks now allows agents to dynamically select the optimal methodology for each task, creating hybrid systems that outperform single-model approaches. Below is a taxonomy of key AI methodologies and the real-world problems they solve:

Core AI Methodologies Powering Modern Agents    
Methodology Problem Type Example Application Model/Technique
Computer Vision Image/Video Analysis Identifying defects in manufacturing QA systems Convolutional Neural Networks (CNNs)
Text-to-Speech (TTS) Audio Synthesis Voice interfaces for banking chatbots WaveNet, Tacotron2
Intent Classification Natural Language Understanding Routing customer support tickets automatically BERT, Transformer Models
Recommendation Systems Preference Prediction Personalized content curation on streaming platforms Collaborative Filtering + Matrix Factorization
Random Forest Multi-Factor Decision Making Credit risk assessment for loan approvals Ensemble Decision Trees
Multi-Factor Decision Making Credit risk assessment for loan approvals Ensemble Decision Trees  
Generative Adversarial Networks (GANs) Synthetic Data Creation Generating photorealistic product images for e-commerce Progressive GANs (StyleGAN)
Transformer Architectures Context-Aware Processing Real-time language translation in video calls GPT-4o, LLaMA2
Support Vector Machines High-Dimensional Classification Spam detection in enterprise email systems SVM with Kernel Trick
Reinforcement Learning Sequential Decision Making Optimizing warehouse robot navigation paths Deep Q-Networks (DQN)
Time Series Analysis Predictive Forecasting Electricity demand prediction for smart grids LSTM Networks

Key Drivers of the AI Agent Revolution

1. Specialized Model Proliferation

The AI landscape now offers purpose-built models for every data type:

Visual Data: YOLOv8 achieves 98% accuracy in real-time object detection

Audio Data: GPT-4o processes speech with 200ms latency for natural conversations

Text Data: LLaMA-3 handles 128k token contexts for legal document analysis

2. Hybrid Intelligence Systems

Modern agents combine multiple techniques:

def process_task(input):
    if input.type == "image":
        return CNN_analyzer(input) [5]
    elif input.type == "text":
        return BERT_classifier(input) [6]
    elif needs_creativity:
        return GAN_generator(input) [7]

3. Reduced Development Barriers

Low-code platforms like Hugging Face Transformers allow rapid agent creation:

Pre-trained models for 100+ languages

One-line API calls for complex NLP tasks.

Real-World Impact by Domain

Healthcare

Problem: Early tumor detection in MRI scans

Solution: CNN-based agents analyze 3D medical images with 94% accuracy

Retail

Problem: Personalized shopping at scale

Solution: Hybrid agents using collaborative filtering + real-time vision:

1. CV identifies clothing items from user photos 
2. Recommender suggests matching accessories 
3. GAN generates virtual try-on previews

Manufacturing

Problem: Production line defect detection

Solution: Vision agents inspect 500+ units/minute using anomaly detection CNNs

The AI agent ecosystem now demonstrates 72% faster task completion compared to single-model systems (Pluralsight 2025), enabled by intelligent model orchestration. As domain-specific fine-tuning becomes streamlined, agents increasingly handle end-to-end workflows - from analyzing supply chain satellite imagery to negotiating procurement contracts via NLP. This modular AI approach positions 2025 as the inflection point where autonomous agents transition from experimental tools to essential enterprise infrastructure.

Types of AI Agents

Overview of agent types such as:

Simple Reflex Agents

Model-Based Reflex Agents

Goal-Based Agents

Utility-Based Agents

Learning Agents

Vertical SaaS AI Agents

  • Cursor
  • Elucidata - Bioinforatics
  • Leena AI - HR
  • Spyne - Automotive

LLMs Meet APIs.

That’s my understanding of what an AI Agent is.

So, why can’t a single LLM do everything? Why doesn’t ChatGPT come in and destroy the entire SaaS landscape?

One of the fundamental principles of prompt engineering is that chained prompts are more powerful than a single prompt.

And, a lot of those specific jobs needs to be on specific data.

For example a Cloud incident detection platform will be combing through specific logs on Cloud services such as AWS, Azure or GCP ChatGPT is not specificallydesigned for reading logs

With Agentic workflows, you can create workflows by leveraging distinct GPTs.

Different agents give different formats

Another principle to prompt engineering is that specifying formats for your LLMs gives you more control over the output.

LLMs

Deepseek, Anthropic, Google, and OpenAI are the most popular LLMs.

The AI agent landscape in 2025 represents a dynamic fusion of advanced language models, adaptive training techniques, and transformative real-world applications. Below, we explore three critical pillars shaping this ecosystem.

1. Leading LLMs Powering Modern AI Agents

The market features a diverse array of large language models, each optimized for specific tasks:

Model Developer Parameters Key Features
DeepSeek R1 DeepSeek 671B Top-tier reasoning for math/coding, open-source, cost-effective training
GPT-4o OpenAI Unknown Multimodal (text/image/voice), 2x faster than GPT-4, Voice-to-Voice latency <320ms
Claude 3.5 Anthropic Unknown 200K token context window, superior coding/vision capabilities
Llama 3.1 Meta 405B Open-source, 15T token training, 128K context
Qwen2.5-Max Alibaba Undisclosed Low-latency performance, excels in code generation and forecasting
Grok-2 xAI 314B Open-source, integrated with X (Twitter), MoE architecture

2. Building Private GPTs: RAG vs. Fine-Tuning

For businesses such as CRMs & Vertical SaaS businesses, LLMs provide different types of opportunities to create specialized GPTs.

Now more than ever before, having proprietary rich data is a competitive advantage. HR-tech software such as Leena AI has been able to create their own private GPT, called WorkLM, on top of which workflows to answer HR queries can be created.

If you are building a private GPT, you have to decide between RAG & Fine-Tuning.

Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation involves two distinct steps.

  1. Storage of special context data in a vector database. Using tools such LangChain and LlamaIndex, you can do appropriate chunking & vectorization of your data. Vectorization is basically creating a vector representation of your data - the text data is converted into a vector of numbers, called embeddings. For a private GPT, this process would require more significant effort. This is because the retrieval process adds context to the LLM - therefore adding the right context is critical
  2. The Generation part relies on an existing LLM - this can be via an API call to OpenAI or Anthropic models, or this can be an LLM privately hosted on your own servers.

Pros:

Cost-effective updates (no retraining)

Reduces hallucinations via real-time grounding

Ideal for compliance-heavy sectors (e.g., healthcare, legal)

Cons:

Slower latency due to retrieval steps

Requires robust vector database management

Fine-Tuning

How it works: Retrains base models (e.g., GPT-4) on domain-specific datasets.

Pros:

Higher accuracy for niche tasks (e.g., legal jargon)

Faster inference after initial training

Preserves data privacy (no external queries)

Cons:

Expensive GPU/TPU requirements

Risk of overfitting to training data

Decision Matrix:

Scenario Recommended Approach
Dynamic data (e.g., news) RAG
Specialized terminology Fine-Tuning
High query volume Hybrid (RAG + FT)

3. Transformative Use Cases for AI Agents

Customer Experience

Sales: AI agents like Find AI automate lead generation with 98% precision.

Support: Chatbase reduces ticket volumes by 65% via refund automation and troubleshooting.

Healthcare

Diagnostics: Google’s AI agent detects skin cancer with 85.4% sensitivity.

Surgery: Robotic systems assist in precision procedures.

Autonomous Systems

Vehicles: Waymo’s self-driving cars use utility-based agents for real-time decisions.

Manufacturing: LG’s EXAONE 3.0 generates Python code for assembly line optimizations.

Marketing & Insights

Personalization: Cisco’s AI agents analyze 140K-token customer journeys for hyper-targeted campaigns.

Competitor Analysis: Talkdesk agents track churn drivers and emerging market trends.

Agentic Workflows, Sustainability - in terms of compute costs & Regulatory compliance are set to be the themes for 2025

The AI agent ecosystem will see three key shifts in 2025:

Agentic Workflows: Tools like Copilot Studio will let non-technical teams build custom agents.

Sustainability: Microsoft’s green AI initiatives aim for carbon-negative data centers.

Regulatory Alignment: RAG’s traceable sourcing will dominate GDPR/CCPA-compliant deployments.

From open-source giants like Llama 3.1 to enterprise-grade RAG pipelines, AI agents are no longer experimental—they’re redefining industries through scalable intelligence.