by Richard Sonnen, Sentinel Director of AI Innovation & Consulting

In the fast-moving world of artificial intelligence, few terms have generated as much excitement - and confusion - as “Agentic AI.” As vendors rush to rebrand their offerings with this latest buzzword, IT leaders are left wondering: What exactly makes an AI system “agentic,” and why should I care?

At Sentinel Technologies, we’re seeing firsthand how this terminology is creating misunderstandings in the market. That’s why we’ve put together this quick guide to help you cut through the hype, understand what Agentic AI really entails, and make informed decisions about its place in your technology strategy.

What Agentic AI Actually Is (And Isn’t)

At its core, Agentic AI refers to AI systems that possess agency - the ability to make independent decisions and take actions toward goals. Unlike traditional AI that operates within predefined scripts or responds solely to direct prompts, truly agentic systems can perceive, reason, and act with a degree of self-direction.

Think of the difference this way: A traditional chatbot waits for your question and provides an answer. An agentic AI might notice you haven’t completed a task, decide to check in, find the information you need, and propose next steps - all without being explicitly prompted.

The key distinction is autonomy. Regular AI tools are like smart hammers that need someone to wield them. Agentic AI systems are more like assistants who can be given a goal and will figure out how to achieve it, making decisions along the way.

The Four Main Types of Agentic AI

Not all Agentic AI is created equal. Understanding the different approaches can help you evaluate vendor claims and identify solutions that genuinely match your needs. Here are the four main types you’ll encounter:

1. Autonomous Decision-Making Systems

These AI agents can make choices and initiate actions without needing a human to approve each step. They operate under high-level objectives or policies, but within those bounds, they decide what actions to take next.

Example in action: An AI-powered trading bot that continuously analyzes market data and executes trades based on predefined strategies, adjusting its approach as market conditions change.

Key characteristics:

• Goal-driven behavior
• Real-time adaptation to changing environments
• Independent action without constant human guidance

2. Planning and Multi-Step Task Execution Systems

These agents can break down complex objectives into sub-tasks, execute those tasks in sequence, and adjust their plan as needed to achieve the final goal.

Example in action: An AI research assistant that, when asked to compile a market analysis, automatically generates a plan: gathering sales data, analyzing trends, drafting a summary report, and emailing it to stakeholders - handling each step by invoking the appropriate tools.

Key characteristics:

• Task decomposition capabilities
• Memory of context across steps
• Tool utilization (calling APIs, controlling software interfaces)
• Reasoning combined with action (“ReAct” approach)

3. Multi-Agent Collaborative Systems

These involve multiple AI agents, each potentially with specialized roles or knowledge, that communicate and collaborate to achieve an overarching objective - similar to a team of human specialists.

Example in action: In cybersecurity, one agent monitors network traffic for anomalies, another analyzes suspicious files, and a third manages response actions like isolating affected machines. Collectively, they handle threat incidents from detection to mitigation.

Key characteristics:

• Division of labor among specialized agents
• Communication protocols between agents
• Orchestration mechanisms to align efforts
• Concurrent operation (agents working in parallel)

4. Persistent Memory and Adaptive Identity Systems

These agents maintain long-term memory, learn from past interactions, and adapt their behavior over time, developing a consistent “identity” or approach.

Example in action: An enterprise virtual assistant that remembers employee preferences, recalls past questions and their outcomes, and uses that knowledge in future decisions—for instance, knowing that Alice had a laptop issue last month and checking if it’s related to her new ticket today.

Key characteristics:

• Persistent knowledge base that grows over time
• Ability to learn from experiences and user feedback
• Adaptive behavior based on historical context - Consistent “personality” or approach to interactions

Real-World Examples: Separating Reality from Hype

With so many vendors claiming to offer “agentic AI,” how can you tell what’s real? Here are some genuine examples of agentic AI in action today, along with red flags that might indicate you’re looking at marketing hype rather than true agency:

Genuine agentic AI examples:

• UI-operational agents: These are AI systems that can actually operate software through the same interfaces a human uses, translating natural language instructions like “Download the sales data and create a summary report in Excel” into mouse clicks and keyboard actions across multiple applications.
• Autonomous Robotic Process Automation (RPA) tools: Advanced RPA systems enhanced with AI that can handle exceptions during processes, like an onboarding agent that encounters a discrepancy and autonomously sends an email for clarification rather than simply stopping.
• Reasoning-action agents: AI systems that interleave reasoning and action, such as a legal research assistant that decides to search for relevant case law, evaluates which cases are most relevant, retrieves them, and produces a summary without human micromanagement.

Red flags that suggest it’s just a chatbot in disguise:

• It never initiates actions without direct user prompting
• It responds based only on the last user message, without using richer memory of past interactions
• It doesn’t interact with external systems or APIs
• Its behavior doesn’t change except through new coding by developers

Remember: If an AI system only produces answers when asked and cannot take initiative or perform external actions, calling it “agentic” is a stretch.

Why Agentic AI Matters to Your Business

The promise of agentic AI isn’t just about having fancier chatbots - it’s about fundamentally changing how work gets done. Here are some ways it could impact different industries:

Manufacturing

• Autonomous production lines that self-adjust based on real-time conditions
• Predictive maintenance agents that schedule repairs before failures occur
• Quality control systems that identify defects and recalibrate machines automatically

Healthcare

• Clinical decision support that monitors patient data and flags emerging concerns
• Personalized care assistants that manage medication schedules and coordinate appointments
• Medical research agents that design and run experiments with minimal human input

Financial Services

• Portfolio management agents that adapt to market conditions in real-time
• Customer banking assistants that proactively manage budgets and bill payments
• Fraud detection systems that can pause cards, verify transactions, and initiate replacements when necessary

Enterprise IT

• Self-healing infrastructure that identifies issues and applies fixes
• Intelligent service desks that resolve tickets end-to-end
• Security operations agents that detect, analyze, and remediate threats autonomously

The key business benefits include reduced operational costs, faster response times, 24/7 availability, and freeing human talent for higher-value work that requires creativity and judgment.

Evaluating Vendor Claims About Agentic AI

When a vendor tells you their solution uses “agentic AI,” here are questions to help you assess whether they’re offering genuine agency or just riding the buzzword wave:

1. Independence test: Can the system initiate actions without explicit prompting? If not, it’s probably not truly agentic.
2. Integration with outside systems: What external systems can it interface with, and how? True agents need to take actions beyond conversation.
3. Adaptation mechanisms: How does the system learn and improve over time? Look for specific answers beyond vague claims.
4. Autonomy boundaries: What limits are placed on the agent’s decision-making, and how are these enforced? Responsible vendors will have clear answers.

A genuinely agentic system should be able to handle an entire workflow with minimal human intervention, while still providing appropriate visibility and controls.

Looking Ahead: Strategic Planning for Agentic AI

In the next 1-2 years, we expect to see significant evolution in agentic AI capabilities:

• Greater autonomy with better guardrails: Systems will make more decisions independently, but within safer bounds
• Multi-agent ecosystems: Standardized protocols may emerge for different agents to communicate and collaborate
• Persistent personal agents: Both consumer and enterprise users will have continuous AI assistants that learn and adapt over time
• Domain-specific agents: Solutions tailored to specific industries or functions, with deep knowledge of relevant workflows

To prepare your organization for agentic AI:

1. Start with pilot projects that are meaningful, but not mission-critical
2. Deploy initially in “co-pilot” mode with human oversight and review
3. Develop clear policies around agent authority, compliance requirements, and fail-safe triggers
4. Consider integration requirements and whether you prefer flexible frameworks or fully managed services
5. Train your teams on how to effectively work with AI agents as collaborators

The Bottom Line for IT Decision Makers

Agentic AI represents a significant leap beyond traditional automation and AI assistants. When properly implemented, these systems don’t just respond to requests, they proactively work toward goals, adapt to changing circumstances, and learn from experience.

That said, the term is undoubtedly being overused in marketing. By understanding the core characteristics of true agency - autonomous decision-making, multi-step planning, potential collaboration with other agents, and persistent memory - you can cut through the hype and identify solutions that offer genuine value.

The most successful implementations will be those that complement human expertise rather than attempting to replace it entirely. AI agents handling routine decisions and actions, with humans providing direction, ethical judgment, and creativity, can achieve far more than either alone.

At Sentinel Technologies, we’re here to help you navigate these decisions and build an AI strategy that drives real business value. Let’s continue the conversation about how agentic AI might fit into your technology roadmap.

If you are interested in learning more about how Sentinel can help your organization with its AI journey, please contact your Account Manager or reach out through our website. Additional details about Sentinel's AI solutions and services can be found at https://www.sentinel.com/AI