Introduction: Beyond the Buzzwords

AI is evolving fast, and so is the terminology. One of the most misunderstood distinctions in 2025 is between AI agents and agentic AI. While many vendors use the terms interchangeably, there's a crucial difference CIOs, CTOs, and customer experience leaders must grasp to avoid overpaying for basic automation and underinvesting in systems that actually scale.

The TL;DR:

• AI Agents = Tools that execute tasks based on narrow instructions

• Agentic AI = Systems that autonomously reason, learn, and orchestrate tasks across multiple tools and goals

Let’s unpack this with real-world implications.

AI Agents: The Specialized Task Runners

AI agents are essentially point solutions. Think of them as glorified macros or rule-based workflows powered by large language models (LLMs). They're good at:

• Resolving a specific type of ticket (e.g., password reset)

• Fetching data from a backend system

• Responding to pre-trained FAQs

But they're limited because:

• They don’t have persistent memory

• They don’t self-reflect or improve

• They can’t chain actions intelligently

Example: A Shopify AI plugin that answers "Where is my order?" by querying the shipping API. It’s helpful, but rigid.

Agentic AI: The Autonomous Reasoners

Agentic AI, on the other hand, is a framework for building goal-oriented systems. It combines memory, reasoning, tool orchestration, and policy enforcement to:

• Decompose goals into subtasks

• Decide which tools or APIs to invoke

• Sequence and adapt actions in real-time

• Improve via self-reflection

Think: Not just answering “Where is my order?” but also checking for delays, issuing refunds, reordering products, and notifying the customer automatically.

Companies like Fini are building real agentic AI experiences that don’t just talk — they act.

Real-World Use Case Comparison

Why This Matters for CIOs and CX Leaders

1. Don’t Buy a Chatbot in Disguise

According to CIO.com, vendors often sell basic chat agents wrapped in fancy words like "agentic." The reality? Most tools claiming autonomy are just calling a database or summarizing documents via RAG.

2. You Need Traceability and Safety

If your AI can initiate actions (e.g., send refunds, change user details), you need to:

• Log decisions

• Set policy constraints

• Audit outputs

Agentic AI platforms like Fini offer these safeguards out of the box, with ISO 42001 readiness and built-in escalation layers.

3. The Future is Autonomous, But Incremental

Agentic AI doesn’t mean full Skynet autonomy on Day 1. Mature teams deploy in steps:

• Start with read-only flows

• Monitor and set guardrails

• Gradually unlock autonomy per use case

That’s why Fini supports hybrid modes: observation, suggestion, confirmation-based action, and full autonomy.

Key Features of Agentic AI Systems

• Persistent Memory: Remembers user history, preferences, and decisions

• Goal-Oriented Reasoning: Not just reacting but solving problems

• Multistep Planning: Executes subtasks across tools without human input

• Self-Reflection: Learns from outcomes and adjusts behaviors

• Autonomy Levels: Fine-tuned control over what the agent can or cannot do

Fini’s agentic loop enables all of this.

Vendor Checklist: How to Evaluate Claims

Subtle, Real-World Agentic AI in Action: Fini

Fini is already deployed in 100+ enterprises. It doesn't just answer questions — it:

• Detects intent ("cancel my account")

• Checks policy compliance (eligibility)

• Updates backend (Stripe, HubSpot, Zendesk)

• Sends confirmations and logs actions

This is real agentic AI. And it works.

Final Thought: Don’t Get Washed

Much like "greenwashing" in sustainability, "agent-washing" is rampant in AI. Ask questions. Dig deeper. Demand:

• Memory

• Reasoning

• Guardrails

• Goal decomposition

And if your AI doesn’t do things? It’s not agentic.

Next Step: Book a Demo with Fini to see agentic AI in your stack.