Agents, Agentic AI, Autonomous workflows. All words you would have been hearing more and more over the last 12 months. It’s a massive focus and one of key pathways that the frontier labs believe is going to change the way we work.
It’s not a product per se, or a version upgrade. Its a fundamentally different way of leveraging the power of LLMs.
Here's what I want you to know right off the bat: you've already used agentic AI. You just didn't know that's what it's called. Copilot Premium drafting a meeting agenda from three words of context? Agentic. ChatGPT's Advanced Voice running a multi-step research thread while you drive? Agentic. Canva generating a full brand kit from one prompt? Agentic. The label is a category catch-up for capabilities that have been shipping under familiar product names for the last year and a half.
So the real question isn't "what is agentic AI." It's what changes when you start connecting these capabilities intentionally.
You're already running agentic workflows. You just didn't call them that. Every time you've asked ChatGPT or Claude to "find the Q2 numbers, check them against last year's, and draft a summary" — that's a multi-step reasoning chain. The LLM interprets your goal, breaks it into steps, gathers context, cross-references, and delivers an output that required more than one operation. That's agentic behaviour, baked into the tools you already have. The frontier models themselves are getting better at it every release - improved context windows, better reasoning, more reliable step-through logic. What you thought was "the tool just getting smarter" is actually the tool learning to execute process. You've been training on this without knowing it if you’ve been getting those at-bats in.
So if this is already agentic, what am I being asked to invest in? Here's the difference: a prompt responds. An agent acts on your behalf.
When you ask ChatGPT to draft a summary, you're driving every step — you set the goal, you evaluate the output, you decide what comes next. When a purpose-built agent — the kind you can configure with today's tools, not buy off a shelf — monitors a grant calendar, it's acting independently: checking new opportunities against your eligibility criteria, drafting a letter of intent, routing it for review, and surfacing to you only when it needs a decision.
You're not investing in a new capability. You're investing the ability to delegate, not just to ask. That means persistent memory (it remembers what it did previously, where it went right or wrong), wide options for tool access (it can read your spreadsheets and draft to your templates), and the ability to hold a thread of thought across hours, days, weeks or months. You're being asked to invest in deliberateness — the infrastructure to make delegation not just sophisticated, but predictable, repeatable, and auditable.
The promise of agentic AI is that your process becomes a product you can break into parts, build, share, and improve. Right now, the way your organization handles grant applications or donor follow-ups or volunteer onboarding lives in people's heads and in a few shared docs. It's invisible. It's hard to audit or evaluate. It disappears when someone leaves. If they are using ChatGPT or CoPilot to support their work, they can impact the output by being intentional in how they feed it or the instruction detail in the prompt, but that’s where their control ends.
Agentic AI changes that: you can encode those workflows — the steps, the decision points, the handoffs — into something the organization owns. That's a real leap. Not "AI does your job." It's "your team's best process becomes a tool everyone can improve and deploy." The value here isn't just efficiency through automation and scale. It's institutional memory that has the capability to act on its own.
The real breakthrough is that agentic AI lets you decompose a process into components — and each component can be improved, evaluated, and audited independently. That's where the value lives, not in the one-shot magic. Yes, it's impressive that you can type "draft the quarterly report" and get back a polished document. But that impressiveness skips over the part that matters for production: the ability to look at step 4 — the data validation step — and say "that's where we keep getting the wrong numbers" and fix just that step without rebuilding the whole thing. The one-shot magic makes a good demo. The decomposition makes a reliable system.
That's why the orchestrator layer is the development that matters right now. Tools like Claude Code, Hermes Agents, and Copilot Cowork aren't just new products — they're workspaces for managing these orchestrations over time. You plug in an LLM, develop niche skills for specific tasks, manage project context, and iterate on workflows across days and weeks, not just within a single session. The difference between a one-shot prompt and a production workflow is the difference between writing a to-do list and building an assembly line. The orchestrator is the factory floor. You don't just run jobs. You build, tune, and maintain interconnected processes. And the value is derived over time, through investment and refinement.
And here's what makes this accessible: you don't need to code these workflows. You can describe them. You can say "I have an idea for an agentic workflow — interview me to help me build this efficiently and break each piece into labels I can understand" and the tool will walk you through the decomposition. The orchestrator translates your natural language into the structure. It will identify when a sub-agent might be required, what skills it could need, and when to spawn it. The barrier isn't technical skill. It's knowing your own process well enough to describe it and refine it.
So where can I go to try this?
If you want to feel what this actually looks like, pick one orchestrator tool — Claude Code, Copilot Cowork, Hermes Agent, whichever fits your stack — and spend an hour this month encoding one real workflow.
And when you sit down to try it, start with natural language. You'll be surprised how far you get by simply describing what you already know.