That said, AI should not be viewed as a monolithic concept. It represents a broad spectrum of technologies, approaches, and applications that are evolving at remarkable speed. Within this expanding landscape, one particular development is gaining notable traction at the strategic level: agentic AI, more commonly referred to as intelligent agents.
1. What Is Agentic AI, Exactly?
The notion of intelligent agents can sound highly technical at first, so let’s break it down in simple terms.
What we commonly call “traditional” AI—often associated with generative AI or large language models (LLMs)—is essentially reactive by design. A user submits a prompt, and the system produces a response. It can generate content, clarify complex topics, or summarize information, but it does not initiate action on its own. Its role is to respond, not to drive outcomes.
Agentic AI introduces a fundamentally different paradigm. Rather than stopping at response generation, it is designed to take action. An agent can evaluate situations, make decisions, orchestrate multi-step workflows across tools or platforms, adjust to new inputs, and work toward a specific goal with a certain level of independence.
In real-world scenarios, an intelligent agent might:
Review an incoming email, interpret the request, open a support ticket, and respond to the customer
Continuously monitor an IT environment, identify anomalies, trigger remediation scripts, and log the resolution
Collect data from multiple sources, analyze and correlate insights, produce a report, and share it with the right stakeholders
These workflows illustrate what is known as the agentic loop: observing the environment, making decisions, taking action, and repeating the cycle while adapting dynamically to changing conditions.
However, like any major technological breakthrough, successfully deploying agentic AI takes time. Fully realizing its value also requires a certain level of organizational readiness and operational maturity.
2. Is Your Organization Ready?
It cannot be emphasized enough: AI is not a silver bullet. Simply introducing AI into your organization does not guarantee positive outcomes. Many companies have rushed into AI adoption without the necessary foundations in place, leading to poorly aligned initiatives, wasted investments, and, in some cases, deeper structural challenges.
Before implementing any artificial intelligence solution, it is essential to step back and address a few critical questions:
What business objectives are you trying to achieve?
Which teams or functions would benefit most from AI-driven capabilities?
What level of return on investment do you realistically expect?
At NOVIPRO, we have identified five distinct maturity levels to help organizations assess where they stand in their AI journey.
Level 0 – Static Systems
No reasoning capabilities or autonomy
Fully deterministic responses to predefined inputs
Basic task automation
Level 1 – Reactive AI (Large Language Model Without Action)
At this stage, the model produces textual responses based solely on a prompt. It has no memory, no ability to take action, and no interaction with its surrounding environment. Its role is limited to generating answers or content.
Example: a conversational chatbot.
Here, the language model is enhanced with access to external tools such as APIs, databases, or automation scripts. While it can trigger actions, its behavior remains tightly governed by predefined rules and workflows. Autonomy exists, but within clear boundaries.
At this level, the system evolves into a true intelligent agent. It can reason about a situation, plan a sequence of actions, execute those actions, observe outcomes, and adjust its behavior iteratively until a defined goal is reached.
Key capabilities include:
Persistent memory
A meaningful degree of autonomy
Decision-making abilities
This stage introduces multiple specialized agents working together within a shared ecosystem. These agents interact continuously to:
Coordinate tasks
Plan collectively
Share contextual information
Solve complex problems collaboratively
This level paves the way for fully distributed and orchestrated AI systems capable of addressing sophisticated, large-scale challenges.
Reaching an agentic level of AI adoption requires more than ambition. Organizations must be able to rely on five foundational technological pillars:
Compute power – sufficient and scalable processing capacity to support advanced AI workloads
Intelligent storage – data architectures designed for performance, context retention, and long-term memory
Integration infrastructure – robust APIs, pipelines, and orchestration layers that connect systems seamlessly
Observability and governance – continuous monitoring, auditing, and drift management to ensure reliability and control
Security and isolation – access management, sandboxed execution, and compliance-by-design
So, is your organization truly ready to move toward agentic AI?
At first glance, agentic AI may appear to represent a radical technological leap. In practice, however, the transition is far more incremental when guided by a clear framework—one that focuses on system connectivity, controlled execution, and strong governance.
This is precisely the approach taken by IBM with watsonx Orchestrate. Rather than aiming to “reinvent” your processes overnight, the platform is designed to make execution more fluid, more consistent, and increasingly autonomous—while remaining firmly aligned with enterprise requirements.
Rather than simply answering a prompt like a traditional LLM, an intelligent agent takes action. It is assigned a goal, identifies the most relevant steps to achieve it, adapts based on intermediate outcomes, and documents each action along the way. This is a shift from content generation to execution-driven intelligence.
In practical terms, Orchestrate makes it possible to:
Delegate repetitive and time-intensive tasks that slow down teams
Coordinate multiple actions across disconnected systems without complex custom integrations
Ensure traceability, auditability, and governance—critical requirements in enterprise environments
Standardize execution regardless of which individual or team initiates the request
IBM uses watsonx Orchestrate internally, particularly to streamline candidate management processes. In this use case, the agent reviews applicant profiles, updates HR systems, prepares follow-ups, and coordinates workflows between recruiters.
The results observed include:
A 40% reduction in administrative workload for HR teams
Faster screening and processing of applications
More consistent and coherent candidate communications
Agentic AI is powerful, but it is not without constraints. An intelligent agent ultimately depends on:
The quality, accuracy, and cleanliness of underlying data
Clearly defined rules and guardrails to prevent inappropriate or unintended actions
Strong governance to avoid drift, silent failures, or infinite execution loops
An infrastructure robust enough to support autonomous, continuous execution
In other words, even the most advanced intelligent agent cannot compensate for an organization that is not ready. Technology alone is not the answer—keeping people at the center remains essential.
At NOVIPRO, we support organizations of all sizes as they navigate their AI journey. We offer tailored training programs to help you identify the solutions best suited to your business needs, along with hands-on expertise to ensure these technologies are integrated effectively and responsibly across your organization.
If you are ready to take the next step and explore how AI can deliver tangible value to your operations, schedule a complimentary consultation with our experts. Together, we’ll assess your current maturity and define clear, actionable next steps.
Would you like to explore more content on this topic?
Top 5 Practical Uses of AI Accessible to Canadian SMEs
Cybersecurity: Adapting to the Age of AI