What is Agentic AI
Agentic AI refers to AI systems that can autonomously pursue goals and act on behalf of a user or organization. Unlike reactive AI which simply responds to inputs, or proactive AI which anticipates needs based on data, agentic AI initiates actions, plans, and makes decisions to achieve a defined objective. This often involves complex reasoning, learning, and adaptation in dynamic environments.
How Agentic AI differs from traditional AI approaches
The key difference between agentic AI and traditional AI approaches lies in the level of autonomy and goal-directed behavior. Traditional AI, encompassing many techniques, often focuses on specific tasks without the inherent drive to achieve a broader objective. Agentic AI, on the other hand, is characterized by its ability to act autonomously and pursue goals proactively. Let’s break down the differences:
| Aspect | Traditional AI | Agentic AI |
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Basic Description
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Solves specific problems with limited capabilities and mostly reacts to input.
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Aims to act autonomously, pursue goals, and adapt to complex environments with long-term planning.
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Focus
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Reacting to inputs, often without memory or planning.
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Pursuing goals actively, adapting strategies, and considering long-term consequences.
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Memory/Context
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Limited or no memory, reacts only to current inputs.
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Can adapt by learning from past experiences and make decisions with an understanding of the world and its goals.
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Planning
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Little to no planning or anticipation of future events.
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Long-term planning and strategic thinking, such as resource management or goal-oriented tasks.
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Autonomy
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Relies heavily on human input or predefined instructions.
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Fully autonomous, making decisions independently in dynamic and unpredictable environments.
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Learning and Adaptation
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Limited learning (e.g., using past data to inform decisions in limited memory AI, but still reacts).
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Continuously learns, adapts, and evolves to optimize performance, often through trial and error.
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Embodiment
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Typically not embodied; interacts within a confined digital space (e.g., chess, limited sensor input).
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Often embodied (e.g., robots), but not strictly necessary; agentic AI could be purely software-based (e.g., financial AI).
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It’s important to note that the sophistication and autonomy exhibited by agentic AI systems varies greatly. Some may be relatively simple, performing limited tasks autonomously, while others can exhibit complex, multi-stage planning and adaptation capabilities.
Use Cases of Agentic AI in action across industries
Can Agentic AI Revolutionize Your Industry?
For businesses managing high-value contracts, invoices, leases, and data-heavy processes, Agentic AI isn’t just an upgrade—it’s a necessity. By automating complex workflows, ensuring compliance, and optimizing financial operations, AI agents save time, cut costs, and minimize human errors.
Let’s explore real-world AI applications that are already reshaping industries handling contracts, finance, legal processes, and data analytics.
1. Contract Compliance Check: Ensuring Accuracy & Risk Mitigation
A. Automated Contract Analysis- Agentic AI reads, understands, and verifies contract terms—ensuring compliance with regulations and business policies. It can instantly detect inconsistencies, missing clauses, or risky obligations, preventing costly legal disputes.
B. Regulatory Compliance Monitoring- With ever-changing compliance laws, AI ensures that your contracts adhere to industry regulations. Whether it’s IFRS 15, IFRS 16, GDPR, HIPAA, or ISO compliance, AI agents track updates and notify stakeholders of required changes.
C. Risk Assessment & Alerts- AI models analyze historical contract data to predict potential risks, flagging unfavorable terms or discrepancies before signing.
Industries That Can Benefit from Contract Compliance Agent-
Agentic AI is rapidly transforming industries that rely on contract-heavy, compliance-driven, and data-intensive processes. Companies in the following sectors stand to gain operational efficiency, cost savings, and enhanced decision-making by integrating AI-powered automation:
- Consulting & Legal Services: Automates contract review, compliance checks, and risk assessments.
- Finance & Banking: AI agents handle invoice verification, fraud detection, and financial risk modeling.
- Real Estate & Leasing: Manages lease compliance, tracks payment schedules, and optimizes renewals.
- Healthcare & Insurance: AI ensures contract compliance, claims verification, and fraud prevention.
- Government & Public Sector: Automates procurement processes, regulatory compliance, and contract lifecycle management.
- Manufacturing & Supply Chain: AI optimizes vendor contracts, payment reconciliation, and operational analytics.
2. AI-Powered Invoice Verification: Eliminating Errors & Fraud
A. Automated Invoice Processing
AI-powered systems extract and validate invoice details against purchase orders (POs), reducing manual intervention. It instantly flags discrepancies, duplicate invoices, or incorrect charges, preventing financial leakage.
B. Fraud Detection & Compliance- AI continuously monitors invoices for anomalies—detecting fraudulent activities, unauthorized payments, and tax compliance violations before they become a liability.
3. Seamless ERP & Accounting Integration- Agentic AI integrates with SAP, QuickBooks, NetSuite, or other ERP systems, streamlining financial reconciliation and reducing processing time by up to 80%
Industries That Can Benefit from AI-Powered Invoice Verification
AI-powered invoice verification is transforming industries that handle high-volume transactions, vendor payments, and financial compliance. Companies in the following sectors stand to gain operational efficiency, fraud prevention, and cost savings by integrating AI automation:
- Finance & Banking: AI agents handle invoice validation, fraud detection, and financial reconciliation, ensuring compliance with regulatory standards.
- Manufacturing & Supply Chain: Automates invoice matching with purchase orders, tracks vendor payments, and detects pricing anomalies to prevent overbilling.
- Healthcare & Insurance: Ensures accurate claims processing, prevents fraudulent billing, and streamlines medical invoice verification for providers and insurers.
- Retail & E-commerce: AI verifies supplier invoices, tracks return-related refunds, and automates discount and tax calculations to prevent revenue leakage.
- Real Estate & Leasing: Manages lease payments, utility billing verification, and compliance tracking, reducing financial discrepancies
- Government & Public Sector: Automates vendor invoice verification, procurement audits, and compliance monitoring for financial transparency.
- Construction & Engineering: Cross-checks project-related invoices, tracks material costs, and prevents fraudulent billing in large-scale projects.
- IT & Telecom: Ensures accurate service billing, prevents overcharges, and automates invoice reconciliation for cloud services and telecom providers.
If your industry relies on invoices, vendor payments, compliance audits, or structured financial workflows, AI-powered invoice verification can enhance accuracy, efficiency, and profitability.
3. AI-Powered Sales Assistant Chatbots: Your Sales Team’s Smartest Ally
Instant Access to Product Knowledge-No more scrambling through documents or waiting for internal approvals—AI-driven sales assistant chatbots instantly provide sales teams with accurate product details, pricing, and competitive differentiators, ensuring confident and informed client interactions.
Real-Time Support for Sales Reps- AI chatbots act as on-demand sales advisors, answering complex client queries, contract-related questions, in seconds. This means faster responses, reduced back-and-forth, and higher conversion rates.
Industries That Can Benefit from AI-Powered Sales Assistant Chatbots
AI-powered sales assistant chatbots are transforming industries that rely on high-touch customer interactions, lead conversion, and sales automation. Companies in the following sectors stand to gain faster deal closures, improved customer engagement, and enhanced sales productivity by integrating AI-driven chatbots:
- Pharmaceutical Manufacturing: AI chatbots assist sales reps by providing instant drug information, pricing details, and regulatory compliance data, ensuring smooth B2B transactions with hospitals and distributors.
- Software & SaaS: AI chatbots handle product inquiries, demo scheduling, and automated follow-ups, ensuring a seamless sales funnel and quicker conversions.
- E-commerce & Retail: Automates customer interactions, personalized product recommendations, and order-related queries, driving higher sales and engagement.
- Finance & Banking: AI chatbots assist with loan applications, investment product recommendations, and compliance-related inquiries, improving lead conversion and customer support.
AI-powered sales assistant chatbots can accelerate deal closures, improve conversion rates, and enhance customer experience
Pain Points Addressed by Agentic AI
Agentic AI offers solutions to several common business challenges. Some are
1. Inefficient Workflows:
Many businesses suffer from complex, slow, and error-prone workflows involving multiple manual steps and handoffs between departments or individuals. This leads to delays, bottlenecks, and reduced productivity. Agentic AI can automate many steps in these workflows, creating seamless transitions between tasks. It can intelligently route information, trigger actions based on predefined rules or learned patterns, and coordinate actions across different systems. A real world example is a logistics company which uses an agentic AI system to optimize its delivery routes in real-time. The system monitors traffic conditions, weather patterns, and delivery deadlines, dynamically adjusting routes to minimize delivery times and fuel consumption, thus streamlining the entire delivery workflow.
2. Data Overload and Decision Fatigue:
Businesses often struggle with overwhelming amounts of data, making it difficult to extract meaningful insights and make timely decisions. This “decision fatigue” can lead to poor choices and missed opportunities. Agentic AI can process and analyze large datasets, identifying patterns, anomalies, and trends that humans might miss. It can then use this information to recommend actions, automate decision-making in routine tasks, or present decision-makers with concise, actionable insights. An example is financial institution that uses an agentic AI system to monitor market trends, analyze risk factors, and autonomously adjust investment portfolios based on predefined risk tolerances and market conditions. This frees up human analysts to focus on more strategic tasks, reducing decision fatigue and improving investment performance.
3. High Operational Costs Due to Manual Processes:
Manual processes in areas like data entry, customer service, and inventory management are expensive and prone to errors. This impacts profitability and competitiveness. Agentic AI can automate these manual processes, significantly reducing labor costs and improving accuracy. This can also free up human workers to focus on more strategic and value-adding activities. An example is a manufacturing company that uses robotic process automation (RPA), a form of Agentic AI, to automate data entry and order processing. This reduces manual labor, minimizes errors, and improves efficiency, leading to significant cost savings and increased throughput.
4. Lack of Proactive Problem Solving:
Traditional systems often react to problems after they occur, resulting in reactive damage control rather than proactive mitigation. Agentic AI can proactively monitor systems, predict potential issues, and take preventative actions. This can significantly reduce downtime, improve safety, and avoid costly disruptions. A real world example is a power company that uses an agentic AI system to monitor the power grid in real-time. The system identifies potential overload situations before they occur and automatically adjusts power distribution to prevent outages, enhancing grid stability and reliability.
Steps to Kickstart Agentic AI Implementation
Here’s a more detailed breakdown of how to kickstart the process:
Step 1. Partnering with the Right AI Development Team or Vendor
Before seeking partners, document your specific needs and goals. What problem are you trying to solve? What are your desired outcomes? Detail the specific tasks the agents will perform, expected levels of autonomy, and the data sources involved. The more detailed your needs, the better partners will understand your requirements and offer tailored solutions.
Then research AI development companies, consultancies, or vendors with experience in Agentic AI or relevant domains. Look for case studies, testimonials, and portfolios demonstrating their expertise in your specific industry. Consider their experience with similar projects, their team’s technical skills, and their approach to data security and privacy.
Request formal proposals that outline their understanding of your needs, their proposed solution, and their project timeline. If possible, arrange a demonstration of their capabilities, showcasing relevant tools and processes.
Step 2. Ensure Stakeholder Buy-in
Implementing AI agents in any department of your organization requires a level of trust from the higher ups and employees. So, determine the individuals and teams who will be impacted by, or benefit from, the implementation. These typically include business leaders, operations teams, IT, and potential end-users.
Clearly articulate how the Agentic AI solution will benefit stakeholders, outlining expected improvements in efficiency, accuracy, cost savings, or other tangible gains. Explain the technology’s potential in simple, non-technical terms.
Demonstrate the initial pilot project’s potential in a tangible way. Show clear, positive results from a limited, targeted implementation.
Step 3. Testing and Refining the Solution Before Full-Scale Deployment
Implement the solution on a small, controlled scale (a pilot project). This allows you to test the solution in a live environment, identify potential issues, and refine the solution before full-scale deployment.
Gather data during the pilot project, evaluating the results against the defined KPIs. Collect feedback from users on the solution’s performance, usability, and functionality.
Use the feedback and data to identify areas for improvement and refine the solution. Adjust parameters, algorithms, and workflows to enhance performance.
Tools and Platforms Commonly Used for the Development of Agentic AI
Developing Agentic AI systems requires a diverse toolkit depending on the specific needs of the project. There’s no single best platform, as the optimal choice depends on factors like the scale of the project, the type of AI models used, the programming languages preferred by the development team, and the existing infrastructure. However, some common tools and platforms are frequently used:
| Category | Tool/Platform | Description |
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Programming Languages
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Python
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Dominant language in AI/ML with extensive libraries (NumPy, Pandas, TensorFlow, PyTorch).
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Java/C++ |
Used for performance-critical components, especially for edge devices.
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R |
Popular for statistical computing and data analysis, often used with Python.
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JavaScript
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Used for front-end development of user interfaces in Agentic AI systems.
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Machine Learning Frameworks
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TensorFlow |
Open-source framework for deep learning, developed by Google.
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PyTorch
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Open-source framework known for dynamic computation graphs, widely used in deep learning.
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Scikit-learn
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Library for ML tasks like classification, regression, and clustering.
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Keras
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High-level API for neural network development, running on top of TensorFlow or Theano.
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Natural Language Processing (NLP)
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spaCy |
Powerful library for advanced NLP tasks (e.g., named entity recognition, POS tagging).
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NLTK
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Library offering tools for text processing and analysis.
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Transformers
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Hugging Face library for pre-trained transformer models (e.g., BERT, GPT).
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Reinforcement Learning (RL)
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Stable Baselines3
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Set of reliable RL algorithm implementations.
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Ray RLlib
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Scalable RL library for training and deploying RL agents on distributed systems.
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Cloud Platforms
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AWS |
Offers services like SageMaker, EC2, and S3 for AI/ML development.
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Google Cloud Platform (GCP)
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Similar services to AWS, with Vertex AI, Compute Engine, and Cloud Storage.
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Microsoft Azure
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AI/ML services like Azure ML, virtual machines, and blob storage.
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RPA Platforms
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UiPath
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Leading RPA platform for automating repetitive tasks in AI systems.
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Automation Anywhere
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Another major RPA platform offering similar automation capabilities.
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Blue Prism
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Third major player in RPA for automating repetitive tasks.
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Development Environments
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Jupyter Notebooks
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Interactive environment for data analysis, model development, and prototyping.
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VS Code
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Popular code editor supporting Python and other languages used in AI/ML.
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PyCharm
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Python-specific IDE with advanced features for debugging and code analysis.
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Growth and Market Trends
Several market research firms predict significant growth in the broader AI market, a significant portion of which will be driven by the increasing adoption of agentic AI capabilities. Gartner reports that by 2028, at least 15% of day-to-day work decisions will be made autonomously through agentic AI.
While precise adoption rates remain elusive, the trajectory suggests rapid future growth. However, the actual pace will depend on overcoming technical challenges, establishing ethical guidelines, and building public trust in the capabilities and safety of these advanced AI systems. Expect to see increasing integration of agentic capabilities within existing AI systems across various sectors in the coming years, rather than a sudden surge in fully autonomous agents. The growth will be a gradual but transformative expansion, rather than a sudden revolution.
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Conclusion
In essence, Agentic AI is not just incremental improvement; it’s a transformative technology that empowers businesses to operate more efficiently, make better decisions, adapt more quickly, and unlock entirely new opportunities for growth and innovation. The companies that effectively leverage Agentic AI will gain a significant competitive advantage in the years to come.
The potential benefits of Agentic AI are clear. But realizing this potential requires action. Don’t just read about the future of automation—be a part of it. Book a call with Sthambh today and get started with your AI development.
The Author
Nikhil Khandelwal
Co- Founder & CEO
