Your IT and business operations are operating at full throttle, functioning like a well-oiled machine; thanks to a platform that brings it all together. It provides observability and AIOps capabilities to solve operational challenges by integrating intelligence, observability, and action. Rather than working in silos, your tools, teams, and data come together to create a smarter, more adaptive operation. One that not only responds to issues but anticipates them and also transforms your operations from reactive to resilient.
This is what a platform built on an agentic architecture can do for your business and ITOps.
But let’s just set that thought aside for now and take a quick look at how IT operations have evolved – before we dive deeper into what Agentic AI means for IT operations.
In this blog, we discuss:
From automation tools (Robotic Process Automation (RPA) software technology) for automating digital tasks swiftly and reliably, to reasoning business analytical tools to analyze data and make informed decisions in a business context, to intelligent automation systems that have the ability to do both reasoning and automation, artificial intelligence (AI) has evolved from being a back-end tool to becoming the brain behind business strategy, operations, and user experiences.
However, given the rate at which the ITOps landscape is changing, one-time automation is no longer sufficient. The platform must incorporate a human-in-the-loop approach, learn from human input, as well as adapt, and generalize. So that as systems change and evolve, the platform doesn’t have to relearn but can build on what it has already learned. It’s important that the “learn, adapt, and generalize” across technologies must happen, and it must happen fast.
This is where Agentic AI comes into the picture.
(To learn more about the evolution of enterprise ITOps, click here)
So, what is Agentic AI?
Agentic AI pushes the limits of intelligent automation with its ability to autonomously understand context, make decisions, perform actions, and continuously learn. These Agentic AI systems collaborate with human experts to address unknown situations and autonomously learn, adapt, and generalize. It responds to inputs, initiates actions, and makes decisions that push the system toward achieving its objectives in an evolving context. These AI capabilities make it ideal for solving some of the biggest IT challenges that enterprises face, while driving greater agility and efficiency.
In addition to proactive behaviors that intelligent automation brings to the table, Agentic AI also has
- autonomy
- decision-making
Let’s take a look at what autonomy, decision-making and proactive behaviors mean in the context of Agentic AI.
What is autonomy in Agentic AI?
Agentic AI can operate independently, making its own choices within a defined scope. It doesn’t need constant human oversight or specific instructions for every action.
What is decision-making in Agentic AI?
Agentic AI uses learned experiences to make decisions on how to proceed, often weighing multiple options to determine the best course of action based on its objectives. This could involve risk assessment, evaluating outcomes, and adjusting strategies as needed.
What is proactive behavior in Agentic AI?
Agentic AI leverages predictive analytics to forecast potential IT failures and performance degradation. By leveraging historical data and AI-driven forecasting, the platform enables enterprises to mitigate risks before they affect business operations.
How does Agentic AI work?
Agentic AI works though the agentic orchestration of the following agents to solve specific tasks:
- Perception agents can interpret context
- These agents use techniques such as context and process mining to establish context from data. They use text mining, natural language processing (NLP), and large language models (LLMs) to extract information from various structured and unstructured data sources, as well as data mining techniques to mine trends and patterns that help baseline normal behavior and generate statistical observations.
- Reasoning agents can reason and make decisions
- These agents use various AI/ML techniques to translate statistical observations into domain-aware insights and recommendations. They make use of episodic, situational, and factual memory to bring domain-awareness and are of different order complexity ranging from simple rule-based agents to complex model-based agents to case-based agents to augmented-intelligence-agents.
- Action agents can execute actions
- These agents range from simple static rule-based agents that automate tasks, to complex dynamic context-driven agents that automate situations.
- Learning agents can continuously learn from interactions
- These agents learn from data, from the system response to actions, as well as from human feedback. They use generative AI (GenAI)–powered conversational intelligence to interact with users and SMEs.
Why Digitate?
Now let’s go back to the beginning of this post where we said, “Imagine this”. With ignio, Digitate’s SaaS platform, you don’t have to imagine; it already exists!
ignio is built on an agentic architecture that can empower businesses with a strategic advantage in managing their IT operations.
The ignio advantage
Like every ignio solution, these agentic solutions are designed with customer needs in mind to deliver the best ITOps transformation tools for enterprises of all sizes. ignio offers five AI Agents to cater to five critical lifecycles services of IT operations:
- AI Agent for Event Management
- AI Agent for Incident Resolution
- AI Agent for Proactive Problem Management
- AI Agent for Business SLA Predictions
- AI Agent for Cloud Cost Optimization
Stay tuned to learn more about these agents in our subsequent blogs.
