Agentic AI: The Catalyst for Smarter, Autonomous Enterprises

What if your software could set its own goals and carry them out without waiting for human input?

Agentic AI’s ability to self-direct basically means it doesn’t require step-by-step instructions to carry out complex tasks. By learning from experience and understanding context, it can make informed choices. The autonomous ability to define its own workflow and interact with external environments to solve complex problems has enterprises excited about its possibilities.

That all sounds wonderful, but how far have we come and what is possible today in practical business terms?

It’s only been in the last two years that Generative AI tools (ChatGPT, Claude, Gemini, DeepSeek, Perplexity, and others) have entered the mainstream in a big way. These tools, driven by large language models (LLM), are great for content creation (code, text, images, audio, video).

Their limits soon became obvious around the requirement for human prompts and no ability to execute multi-step workflows autonomously. Agentic AI fills this gap by making decisions and acting independently.

You can find multiple surveys of IT leaders saying anywhere from 30 to over 50 percent of their respondent organizations are adopting or deploying Agentic AI over the next two years. But like every other advancement, its only as useful as its ability to transform real business outcomes. So, we should start with understanding what it really is, how it’s structured, and its current real-world capabilities.

What is Agentic AI?

The first thing to understand is Agentic AI refers to a broader system that is driven by AI agents (AI-driven software programs), where each agent has specific capabilities, such as:

• Retrieving knowledge used for topics and environments with less need for regulation
• Prescriptive knowledge agents for highly regulated environments and topics
• Workflow agents that execute multi-step processes and often call other systems, APIs, or databases on the fly
• User assistant agents to help individuals manage day-to-day tasks or requests with no need to start from scratch every time

Agentic AI’s structure is a system of systems. Each agent is purpose-built to handle specific functions like perception (gathering data), reasoning (analyzing and deciding), and action (executing tasks). They work individually, but also together in orchestrated flows to complete complex objectives.

The architecture is built around modularity, scalability, interoperability through agent communication, and reinforcement learning to improve decisions and outcomes.

These systems organize agents into subagents and primary agents. Subagents focus on specialized tasks while a primary agent oversees them, manages coordination, and ensures outputs from each agent feed into the next step in the workflow.

Agentic AI’s ability to handle increasingly complex operations with minimal human input or oversight requires clear design based on:

• Specific intent, workflows
• Careful integration/implementation into the broader IT architecture

With that perspective, let’s look at some of today’s real-world use case possibilities.

Use Cases in Enterprise Environments 

Like generative AI before it, Agentic AI is no silver bullet for every situation. The key is finding the right use cases where its proven strengths can apply to real-world business outcomes. These outcomes would be based on speed, efficiency, accuracy, and analysis via specific actions within complex processes and environments.

IT Operations

Agentic AI is the latest tool battling IT architectures complexity. It does this by bringing a level of autonomic, proactive, and adaptive management to workflows. This can include AI-powered self-healing systems for sufficiently autonomous vulnerability remediation and incident resolution. These and other applications work to reduce downtime, increase efficiency, improve security, optimize performance and lower costs.

Customer Support

Gartner predicts Agentic AI will resolve 80 percent of customer service issues by 2029. The big question is, where do you apply it to get the best results?

Agentic AI systems can integrate goal-specific AI agents with third-party apps, such as CRM, ERP, communication tools, and platforms to collect relevant data. They can each do their part in complex customer support process for:

• Customer service ticket resolution
• Consumer data analysis
• Handle complex interactions with and without human agents

This can play out in countless ways across different customer support workflows and interactions. It depends on the level of Agentic AI system integration with existing applications, platforms, and systems.

Cybersecurity

Agentic AI and AI agents can transform security and threat management by bringing a higher level of autonomy to complex areas like:

• Ensuring data quality and pipeline optimization for data management
• Evidence gathering to drive alert priority and authentication, documenting each step in analysis and pushing prescribed response mechanisms
• Proactive threat hunting
• Monitoring internal and external sources for vulnerabilities

Finance and Back Office

Agentic AI transforms Back-office team and functions spanning finance, HR, legal, IT, and operations where data volumes and compliance process challenges are greatest. Agentic AI can operate in similar ways on a broader scale for BFSI, mortgage, and capital markets sectors.

The reality of moving from the possible to the practical becomes clearer with examples of how sectors can and are using Agentic AI today.

Agentic AI Industry Use Case Examples

TELCO

A telco can automate tier-1 NOC tasks by leveraging Agentic AI to streamline operations and improve efficiency, including:

• Continuous network performance metrics monitoring, anomaly detection, and alert generation 
• Situational and root cause analysis coupled with automatic corrective action
• Predict potential equipment failures and proactively schedule maintenance
• Monitor resource utilization, identify bottlenecks, and automatically adjust resource allocation to optimize performance and reduce costs 

By automating incident resolution and resource management, agentic AI can contribute to building self-healing networks, which can automatically identify and resolve issues with minimal human intervention. These Agentic AI approaches reduce manual intervention in complex systems, lower operating costs, improve predictive maintenance and uptime, and enhance customer satisfaction across complex systems.

BFSI, Personalized Financial Planning and Capital Markets use Agentic AI for:

• Real time transaction and market trends analysis to spot anomalies rule-based models miss
• Instant blocking of fraudulent activity
• Automatic portfolio adjustment to protect investments and maximize returns
• Real-time data analysis for actionable investment insights
• Autonomous regulatory report generation
• Automatic assessment of every transaction against global regulations
• Flagging suspicious activity and proactively alerting compliance teams

Agentic AI in Healthcare

Over 40 percent of total hospital expenses are administrative costs, according to the American Hospital Association. Healthcare AI agents can bridge the gap between administrative and care time and cost savings through:

• Processing and analyzing compound screening and testing across research and care
• Claims processing and billing
• Real-time supply chain management
• Care coordination across departments

Medical device manufacturing can also see many benefits from analyzing huge data streams from thousands of devices to:

• Simulate potential failures
• Track usage, performance, and service parameters through automated complaint management tracking and analysis
• Enable autonomous predictive modeling to inform design, features, predictive maintenance
• Drive automated compliance monitoring and auditing

Benefits to Business

Every sector with a sound planning and implementation strategy can achieve big Agentic AI benefits like:

• End-to-end automation of complex processes and workflows
• Multiple agent collaboration coordinated by a management agent to handle complex, interconnected tasks efficiently
• Autonomous response adaptation to changing scenarios and variables based on historic internal and external sourced data analysis
• Proactive issue resolution such as timeline adjustments, threat/risk detection and real-time supply chain adjustments

The foundation of any Agentic Ai strategy is accounting for implementation challenges.

Implementation Considerations

Organizations must have a sound strategy for dealing with challenges integrating Agentic AI systems into their operations, starting with:

• Document business processes, workflows, and knowledge to determine the best place to apply Agentic AI
• Organize IT and data infrastructure to determine insufficient data management and accessibility, Cloud architecture, API use, and IT architecture integration, observability, and management
• Define human and machine integration to balance autonomy with oversight for agent output validation, control and governance mechanisms and regulatory compliance boundaries.
• Adopt change management practices to smooth the workflow and process transition while providing opportunities for training and growth
• Define clear guardrails and governance practices, systems, and processes to drive agentic AI implementation and use like audit trails and predefined decision boundaries

Moving Beyond Agentic AI Hype to Practical Help with Solugenix

At Solugenix, we partner with you to move beyond the hype of Agentic AI to the reality of what is possible, practical, and drives real business outcomes. Most solutions today are semi-autonomous rather than fully autonomous, but they can still deliver important business benefits to organizations. The goal is to make better products, services, and workflows. We start by asking, does it serve the users better?

We focus on partnering with organizations to help guide Agentic AI adoption in our specific sector areas of expertise, which include:

• Contact and support center services transformation, where Agentic AI is a natural next step to current AI, automation, and RPA integration.
• IT Ops/NOC where ITSM transformations are taking hold with various AI solutions means even semi-autonomous Agentic AI can yield massive benefits.

The same approaches apply in healthcare, BFSI, capital markets and retail sectors where we have a long history of innovation and digital transformation success.

Agentic AI has a long evolutionary road ahead.

Our goal is to help organizations plan and implement it within digital transformations that deliver futureproof workflows and business outcomes. We build our approach on understanding the difference between Agentic AI hype and real outcomes based on the proven reality that:

• Real results mean building specialized agents for different tasks that communicate with each other
• Provider-side agents optimizing backend operations deliver the real ROI
• Successful implementations still have humans in the loop to improve their teams and workflows while minimizing risk
• Agent collaboration and orchestration is the affordable, incremental future rather than a single complex agent

What the Future Holds

The near future of Agentic AI and AI agents are the multi-agent ecosystems with agents collaborating with each other to achieve complex objectives through orchestration. Multimodal AI (which we will discuss in the next blog) will begin merging with RPA to enable seamless, end-to-end workflows with unified multimodal processing.

Agentic AI marketplace platforms will connect buyers and sellers of AI-powered agents for easier discovery of what works for specific business needs. A current example is Hugging Face enabling teams to upload, find, and download pre-trained AI models in over 35 different tasks and use them with simple API calls.

Other platforms and applications with embedded Agentic AI and AI agent orchestration are already being introduced. These include pre-built agentic AI platforms, hardware, frameworks, and tools from major tech vendors. Some include ServiceNow’s AI Agent Orchestrator and various cloud provider solutions.

AutoGPT helps teams with creation, deployment, and management of AI agents that automate complex workflows. Others like LangChain’s open-source frameworks offer rapid deployment and measurable return on investment. These platforms will enable groups of specialized AI agents to work together in achieving specific complex goals.

Understanding where we are with Agentic AI and where we’re going can make for a complex roadmap for any organization. Plotting the right course based on specified workflows and business outcomes is about moving from decision support to decision execution.

Are your systems ready to think and act on your behalf?

Contact the experts at Solugenix for a consultation to explore pilot opportunities.