Agentic Orchestration: The Career I Didn't Know I Was Training For
I've spent my career teaching computers to do things in the right order. It sounds simple. It is not. It started at a large university where I was tasked with digitizing — well, everything. Applications, enrollment, meal plans, online payments, transcripts after graduation. Every step a student touched from "I'd like to attend" to "I have a degree now, please" had a paper trail, and my job was to eliminate it. If you've ever tried to get a university to agree on a single workflow for anything, you know this is less of a technology problem and more of a diplomatic mission. But we did it. Every form, every approval chain, every "but we've always done it this way" — digitized and orchestrated. From there, I moved into a world where getting the order of operations wrong isn't just inconvenient — it's dangerous. I worked on orchestration systems for Air Traffic at the FAA. When you're coordinating thousands of flights, the margin for error lives somewhere between "zero" and "absolutely not." You learn to think in systems. You learn that orchestration isn't just automation with a fancy name — it's the discipline of sequencing dependent tasks, managing state, handling failures gracefully, and making sure the whole thing doesn't fall over when one piece misbehaves. So when I tell you that what's happening right now with AI agents and orchestration is the most remarkable technology shift I've encountered in my career, I need you to understand the baseline I'm comparing it to.
From Workflows to Agents
For decades, automation meant defining rigid paths. If this, then that. Step one feeds step two. A human designs the flow, a computer follows it, and if anything unexpected happens, the whole thing throws an error and waits for someone to fix it. We got very good at this. Entire industries run on it.
But AI agents are a fundamentally different animal. An agent doesn't just follow a script — it reasons about what to do next. It can read a document, decide what information matters, take an action, evaluate the result, and adjust. Chain several of these agents together with an orchestration layer, and you have something that starts to resemble how teams of humans actually work: specialized roles, handoffs, judgment calls, and course corrections.
This is agentic orchestration — the coordination of multiple AI agents, each with distinct capabilities, working together to accomplish complex tasks that no single agent (or single automation rule) could handle alone.
And here's what makes it so powerful: the orchestration patterns aren't new. Sequencing, parallelization, error handling, state management, retry logic — those of us who've spent years building workflow systems recognize all of it. What's new is that the individual workers in the system can now think.
Every Keyboard and Mouse Has Exposure
In my talks, I make a claim that tends to raise eyebrows: any job that primarily involves a keyboard and mouse has exposure to AI. Not "will be replaced by AI" — exposure. Meaning AI can assist, augment, or automate meaningful portions of that work.
I'll be honest. When I first started saying it, I thought I was being a little provocative. Turns out I was being conservative.
Anthropic's research on AI exposure by occupational category paints a striking picture. The theoretical AI coverage extends deeply into fields like computer and math, business and finance, legal, education, arts and media, and architecture and engineering. But here's the interesting part — look at the gap between theoretical coverage and observed usage. In almost every field, there's a canyon between what AI could do and what it's actually doing today.
That gap? That's the orchestration problem. The raw capability is there. What's missing is the connective tissue — the systems that coordinate agents, route tasks, manage context, and handle the messy reality of real-world work. Individual AI models can write code, summarize legal documents, analyze financial data, and draft marketing copy. But getting them to do these things reliably, in sequence, as part of a larger process, with appropriate human oversight — that's orchestration.
And that canyon is closing fast.
The Old Guard's Advantage
Here's the part that might surprise you: the people best positioned to lead the agentic AI era aren't necessarily the ones who understand large language models the best. They're the ones who understand orchestration.
If you've designed workflow engines, built ETL pipelines, managed job schedulers, or — heaven forbid — tried to get a university's financial aid office and registrar's office onto the same automated system, you already think in the patterns that matter. You know how to decompose a complex process into discrete steps. You know why idempotency matters. You know what happens when you don't plan for partial failures. You've probably got the scars to prove it.
AI agents are powerful new workers. But workers without coordination are just chaos with a budget. The orchestration layer is where the real value gets created — and where the real expertise is needed.
Looking Ahead
We're at an inflection point. The theoretical AI coverage that Anthropic's research describes is rapidly becoming observed coverage as agentic systems mature. The fields with the largest gaps today — healthcare, social services, education — aren't lagging because AI can't help. They're lagging because nobody has built the orchestration yet.
For those of us who've spent careers wiring systems together and making sure step 47 doesn't run before step 46 finishes, this moment feels less like a disruption and more like a homecoming. The tools are better. The workers are smarter. But the job is the same one it's always been: get the right things done, in the right order, without dropping anything important.
And maybe, just maybe, without anyone having to fax a transcript.