Agentic AI: What It Is and Why I’m Keeping an Eye on It
I’ll be honest—when I first heard the term “agentic AI,” I rolled my eyes. Another buzzword? Another “autonomous assistant” that promises to do everything short of making coffee? But after digging into the frameworks and watching some early dev builds in action, I’ve started to see where this could actually shift the way we work.
Why I Started Looking Into Agentic AI
It was during a late-night documentation sprint last quarter—juggling markdown edits, Jira updates, and a half-broken PowerShell script—that I realized how much of my workflow is still reactive. Most AI tools I’ve used are great at answering questions, but they don’t do much unless I spoon-feed them every step.
Agentic AI flips that. These systems are designed to take initiative. You give them a goal, and they figure out the steps—whether that’s booking a meeting, updating a dashboard, or even debugging a chunk of code. It’s not just automation; it’s autonomy.
What Makes Agentic AI Different (From the Stuff We’ve Already Tried)
Here’s what stood out to me while testing a few dev builds and reading up on the architecture:
- Goal-Oriented Behavior: Instead of “respond to this input,” it’s “achieve this outcome.” That’s a subtle but powerful shift.
- Tool Integration: Some agents can browse the web, use APIs, and even interact with apps like Notion or Slack. I tried one that could navigate a CRM interface—clunky, but promising.
- Memory and Context: The better ones remember what they did five steps ago. That’s huge for multi-step workflows.
- Self-Correction: I watched an agent retry a failed spreadsheet edit three times before switching tools. Not perfect, but it didn’t just crash and burn.
- Planning and Sequencing: They don’t just react—they plan. That’s what makes them feel more like junior admins than chatbots.
Where I’ve Seen It Used (Or Tested)
I haven’t deployed agentic AI in production yet—most of what I’ve seen is in beta or dev environments. But here’s where it’s already showing up:
- Ops and Reporting: Automating weekly reports, syncing data across platforms. I saw one agent handle a Salesforce-to-Google Sheets sync with minimal handholding.
- Customer Support: Agents that can navigate help centers, escalate tickets, and follow up without human nudges.
- Dev Workflows: Code review, bug triage, even writing test cases. Cursor AI and Replit are doing interesting things here.
- Healthcare Admin: Intake forms, appointment scheduling, and documentation—especially in clinics with limited staff.
- Education and Tutoring: Personalized lesson plans and grading assistance. Not gonna lie, I wish I had this back in my training days.
How Well Does It Actually Work?
Let’s not sugarcoat it—results vary. Here’s a rough snapshot from recent tests:
| Platform | Success Rate | Capabilities |
|---|---|---|
| ChatGPT Agents | ~80% | Tool use, memory, web browsing |
| Google Operator | ~50% | Interface navigation, doc editing |
| Claude Agents | ~30% | Reasoning with safety-first design |
I ran a few spreadsheet tasks through ChatGPT agents and got decent results. Google’s Operator felt more experimental—good UI control, but flaky on logic. Claude was cautious to a fault, which makes sense given its safety-first approach.
Who’s Building This Stuff?
If you’re curious about where to start, here’s who’s leading the charge:
- OpenAI: ChatGPT agents with tool use and memory.
- Microsoft: Agent Factory—still early, but looks enterprise-ready.
- Google DeepMind: Project Mariner, focused on agentic systems.
- Anthropic: Claude agents, built for reliability and safety.
- LangChain & AutoGPT: Open-source frameworks for custom agents.
I’ve tinkered with LangChain locally—great for prototyping, but you’ll need to wire up your own tools and memory modules.
Lessons Learned and What to Watch For
- Don’t expect magic: These agents still need guardrails. I’ve seen them loop endlessly or misinterpret vague goals.
- Tool access matters: The more APIs and integrations you give them, the smarter they seem.
- Memory is a game-changer: Without it, they’re just fancy chatbots.
Final Thoughts
Agentic AI isn’t just another layer of automation—it’s a rethink of how we delegate digital work. I’m not replacing my scripts or dashboards just yet, but I’m definitely watching this space. If you’re running workflows that involve repetitive, multi-step tasks, it’s worth exploring—even if just in a sandbox.
Ever tried wiring up an agent to handle your weekly ops report or triage stale Jira tickets? I’d love to hear how it went—or what blew up. Drop a comment or ping me on LinkedIn.
