Understanding the revolution
Agentic AI:
Much more than a chatbot
- Definition
What is Agentic AI ?
The term “agentic” comes from the word “agent”: an entity that acts in the world. Agentic AI refers to artificial intelligence systems with the capacity for autonomous action and continuous adaptation to their environment.
Where a traditional automation tool follows fixed and pre-established rules, an AI agent reasons, adapts to new situations and finds the best way to accomplish its mission.
That’s the difference between an automaton and an intelligent collaborator.
An AI agent is a system capable of perceiving its environment, reasoning, planning, and acting autonomously to achieve a defined objective.
Unlike classic conversational AI, an AI agent does not simply answer a question: it makes decisions, uses tools, interacts with external systems, and executes complex sequences of actions, often without human intervention.
- Comparison
Classical AI vs. Agentic AI
Understanding the difference means understanding why agentic AI is changing everything for businesses.
Reactive and limited
- Answers a question, then stops
- Cannot operate within external systems
- Requires human intervention at every stage
- Significant difficulty in breaking down and planning complex tasks
- No contextual learning in production
Autonomous and results-oriented
- Plans and executes complex sequences of actions
- Persistent memory and continuous learning
- Interacts with your tools, APIs, and databases
- Works independently towards a goal
- Decomposes and solves multi-step problems
- Improves with each interaction and feedback
- Functioning
The 4 pillars of an AI agent
Every effective AI agent relies on four fundamental capabilities that allow it to act intelligently and autonomously.
- Pillar 1
Perception
The agent observes and collects information from its environment: data, documents, system signals, messages, real-time events.
- Pillar 2
Planning
It reasons about the situation, breaks down the objective into sub-tasks and develops an optimal action plan, taking into account constraints and priorities.
- Pillar 3
Action via tools
The agent uses tools (APIs, databases, ERP systems, email, web…) to execute its decisions and interact with the real world.
- Pillar 4
Memory & Learning
It memorizes the context, learns from each interaction, and continually refines its decisions to become more effective over time.
- Concrete examples
What AI agents do in business
Real-life examples of what our agents achieve in different sectors.
- Manufacturer
Real-time quality control agent
The agent analyzes data from production sensors continuously, detects anomalies before they become defects, triggers automatic adjustments and alerts managers.
- Scrap rate reduced by 38% in 3 months
- Logistics
Supply Planning Officer
It monitors stock levels, anticipates stockouts through predictive analysis, and automatically generates and sends purchase orders to suppliers according to defined business rules.
- −27% stockouts, −18% overstock
- Professional services
Customer Request Processing Agent
It reads, understands and classifies incoming requests, answers standard questions, escalates complex cases to the appropriate human agents and ensures complete follow-up on each case.
- 70% of requests processed without human intervention
- Finance & Accounting
Automated Financial Reporting Agent
The agent collects data from multiple sources (ERP, banks, Excel), generates monthly reports, detects anomalies and distributes dashboards to stakeholders.
- 8 hours/week saved per finance team
- Human Resources
Candidate pre-selection agent
It analyzes incoming CVs according to your criteria, performs an objective pre-selection, automatically schedules interviews and generates summaries for recruiters.
- Recruitment time reduced by 50%
- Industrial maintenance
Predictive Maintenance Agent
By monitoring equipment data (vibrations, temperatures, consumption), the agent predicts breakdowns before they occur and plans interventions at the optimal time.
- 45% reduction in unexpected breakdowns, 30% reduction in maintenance costs
- Vision
Agentic AI:
a revolution in progress
The pace of change is rapid. Companies that position themselves in this area today will gain a decisive advantage in the years to come.
VIA Agentic: at the forefront from the start
As a division of VIA Communication, we have been following the evolution of AI since 2016 and were among the very first in Quebec to develop agent-based solutions for businesses. Our technological lead is your competitive advantage.
- 2022–2023 · The Beginnings
The era of LLMs and early agents
Emergence of major language models (GPT-4, Claude) and the first AI agents capable of using tools.
- 2024–2025 · The Rise
Autonomous agents in production
AI agents are being deployed in real-world production environments, with measurable results. This is the decisive phase. The best time to act is now.
- 2026–2027 · Horizon
Generalized multi-agent systems
Collaborative agent networks managing entire segments of business operations. Agent-based organizations will be the norm, not the exception.
- 2028+ · Near Future
The fully augmented company
Each function of the company (HR, finance, production, sales) will be assisted or controlled by highly specialized agentic systems.
- Ready to take action?