Agentic AI, Explained: AI That Thinks and Acts
- Leoza Kabir Barker
- Jul 7
- 4 min read
We’ve all seen what Generative AI tools like ChatGPT and Copilot can do, summarize documents, write emails, generate reports. But lately, a new term has been making the rounds: agentic AI. While AI agents is the broader term, agentic AI refers specifically to systems that go beyond answering questions — they take initiative, plan, and act across multiple steps to achieve a goal.
It sounds fancy, maybe even futuristic. But it’s a very real shift in how AI works and how we’ll work with it.
If you’re just starting to explore AI, this post will help you understand what agentic AI is, how it works, where it’s already being used, and why it’s not about replacing your jobs but empowering you.
Agentic AI is more like hiring an intern who takes a goal and figures out how to get it done across multiple steps, adapting along the way.
Agentic AI isn’t about smarter answers, it’s about smart execution. You give it a high-level instruction, and it figures out the steps, tools, and logic required to carry it out.
How Agentic AI Works (Simple Breakdown)
Most agents follow a basic loop:
Perceive – Understand the goal and gather relevant data (from CRMs, files, transcripts, etc.)
Plan – Break the goal into actionable steps.
Act – Use available tools (like APIs or Power Automate) to execute each step.
Observe & Adapt – Evaluate results, learn from failures, and retry or improve.
And then repeat until the goal is achieved.
This loop is what makes agentic AI feel different. It’s not just predicting the next word, it’s making decisions, taking action, and adjusting in real time.
Real-Life Use Case: My Experiment with Copilot Studio
To bring the concept of agentic AI to life, I built two agents using Copilot Studio to support project management workflows:
Risk & Issue Agent – This one scanned meeting transcripts to extract relevant risks, issues, and changes, surfacing them in a structured format.
Status Report Agent – This one generated weekly project status reports and emailed them to project managers.
Both agents followed the key pattern of agentic AI:
They received a high-level goal (e.g., “Summarize this transcript” or “Send a status report”)
They planned what steps were needed to complete it
They executed actions using Power Automate and connected data sources
They observed results (e.g., success/failure of email delivery) and could be configured to adapt if something went wrong
Instead of waiting for a single prompt and returning an answer like traditional AI, these agents were built to act with autonomy, taking a goal and working across multiple tools and systems to complete it. And they improve through a continuous feedback loop.
This is the core of what defines agentic AI, not just smart responses, but coordinated execution, multi-step reasoning, and integration with real-world tools.
Where Agentic AI Is Already Being Used
Agentic AI is more than a lab experiment, it’s already reshaping work:
Customer Support – AI agents handle inquiries end-to-end, escalating only when needed.
Sales & Marketing – Agents personalize content, generate campaigns, and automate follow-ups.
Operations – Monitor systems, detect anomalies, and initiate resolution flows automatically.
DevOps & IT – Handle routine deployment, health checks, and triage across environments.
Project Management – Generate summaries, status reports, risk breakdowns, and reminders.
Research & Strategy – Assist in gathering insights, comparing sources, and drafting briefs.
Companies like NVIDIA and Salesforce are already building agentic frameworks into their ecosystems. In fact, Salesforce’s Agentforce platform is helping companies build agents that integrate deeply with CRM, data, and automation layers, claiming results that are 16x faster to build and 75% more accurate than starting from scratch. Organizations like Microsoft currently has over 500 live agents in over 40 languages seeing thousands of hours of productivity gained.
But What Are The Risks
Like any powerful tool, agentic AI isn’t without challenges:
Loss of Control – Agents can take unexpected actions if goals aren’t scoped properly.
Security & Access – Since agents operate across systems, access control and data privacy are critical.
Transparency – It’s not always clear why an agent made a decision, which raises accountability concerns.
Dependence on Underlying Logic – A smart agent on top of messy processes will still produce messy results.
HBR calls this the “moral crumple zone” when things go wrong, humans still get blamed, even if the AI was technically the one acting.
Agentic AI represents a meaningful evolution in how we interact with technology. It’s not just about smarter answers, it’s about building systems that can take action, adapt, and support real business outcomes.
But let’s be clear: it’s not magic.
Behind every effective agent is a foundation of:
Clear objectives
Structured logic
Secure data access
Workflow orchestration
Error handling and recovery
Agentic AI doesn't replace the need for developers, analysts, or automation experts, it amplifies what they can do. It lets us focus more on strategic thinking and less on repetitive execution.
Whether you're building with Microsoft 365 Copilot, Copilot Studio, or any enterprise tool, you're not just experimenting with AI, you're discovering a new level of productivity.
About Me
I'm Leoza Kabir Barker, a Functional Architect at Alithya with a focus on the Power Platform. Through my expertise, I aim to streamline processes, optimize operations, and maximize productivity.
Connect with Me
Blog: www.leozakabirbarker.com
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