There’s a common phrase in tech: “Skate to where the puck is going, not where it’s been.” But for the past two decades, at Softheon and now CITIZ3N, we weren’t chasing the puck. We were the puck. And now, the industry is finally catching up.
For 25 years, we’ve architected systems that operate independently—backend agents that process enrollments, verify eligibility, manage billing, and reconcile payments, all without needing to be micromanaged by humans. In our Marketplace platform alone, nearly 1,400 of these backend agents run continuously, handling complex healthcare transactions on behalf of state agencies.
But for a long time, we didn’t talk about them as agents. That term felt too abstract. The market preferred words like BPM, middleware, or more recently, RPA. So we stayed quiet and focused on building systems that worked.
Now, with the rise of Agentic AI, the rest of the industry is finally using the language—and building the architectures—we’ve operated in for years.
What Is Agentic AI, and Why It Matters
In Agentic Artificial Intelligence by Pascal Bornet, Yonatan Bisk, Charles Isbell, Michael Littman, Peter Stone, and others, the authors define agentic systems as AI-driven agents that can plan, act, learn, and evolve toward goals—often with little or no direct human input.
The book lays out five levels of AI autonomy:
- Rule-Based Automation – if-then logic; static decision trees
- Assisted AI – task automation with some machine learning
- Agentic AI – agents that plan and execute multi-step workflows
- Self-Learning Agents – adapt and evolve without reprogramming
- General AI – creative, contextual, and autonomous at scale
Most agencies today sit between Level 1 and Level 2. But state healthcare systems don’t have the luxury of moving slowly anymore.
CITIZ3N Has Been Operating at Level 3 and Beyond
When we launched CITIZ3N as a standalone business unit focused on government, it wasn’t to pivot. It was to scale a model that was already working.
From ACA Marketplaces to Medicaid eligibility, our platforms already:
- Plan and run multi-step logic across workflows.
- Adapt in real-time to policy changes, data inputs, and system exceptions.
- Execute autonomously, reducing burden on agencies and minimizing error-prone manual work.
This is not a prototype or pilot. It’s running live—today—in multiple state environments.
Why It’s Time for Governments to Catch Up
If you’re a government leader and your systems are still based on static rules, batch processing, or multi-vendor middleware chains, you’re not just behind the puck—you’re skating away from it.
The public sector is notoriously slow to adopt innovation—but agentic AI isn’t innovation anymore. It’s infrastructure.
And if you’re not exploring these systems now, you may find yourself reacting to them later—while others are scaling, automating, and delivering better services with fewer resources.
Final Thought
Agentic AI isn’t the future—it’s the present. And for Softheon & CITIZ3N, it’s the past, too. We’ve been quietly building systems that fit the definition long before the industry had a word for it.
So if you’re asking whether your agency is ready for the AI era—the better question might be:
Are you ready to catch up to what’s already working? We’d love to show you how.
