Maybe We Shouldn’t Call Them AI “Agents”

Beware of pretty faces that you find. A pretty face can hide an evil mind.
– Johnny Rivers, Secret Agent Man

As artificial intelligence capabilities expand into government service delivery, it’s worth pausing to think carefully about the language we’re using. The terms “agentic services” and “agentic AI” have gained significant traction in the tech industry, and for good reason — it captures something important about AI systems that can act autonomously. I myself am as guilty as anyone of using this term frequently. But for those of us working in government contexts, there are some considerations worth keeping in mind.

The “Agent” Problem in Government

In government, the word “agent” carries particular connotations. FBI agents. Border patrol agents. IRS agents. These are enforcement and investigative roles. When citizens hear “government agent,” they often think of authority, compliance, and oversight — not helpful service delivery.

This isn’t an insurmountable problem, but it’s worth being aware of. The language we choose shapes how citizens perceive and respond to new service models. If we’re trying to build trust in AI-enabled services, starting with terminology that might trigger concerns about surveillance or enforcement may not be ideal.

(And yes, for a certain generation, The Matrix movies didn’t exactly help the cultural perception of “agents” either. 😅)

What the term “agents” might obscure

There’s a deeper consideration beyond just the word “agent” itself. Calling these services “agentic” can make them sound radically new — a complete departure enabled by cutting-edge AI. But that framing might obscure an important reality.

Delegation-based government services aren’t new. They’ve existed for decades, and are extremely common today.

Tax preparers handle filing returns on behalf of clients. Immigration attorneys navigate visa applications. Customs brokers manage import/export documentation for businesses. Permit expediters guide building approval processes. Benefits navigators help people apply for disability or veterans services.

These are all delegation relationships. Citizens hand over complex, high-stakes government interactions to trusted specialists who handle the administrative burden on their behalf. AI doesn’t enable this service delivery paradigm, but it does potentially make it more scalable and affordable.

Why Words Matter

Thinking about these services as “delegation-based” rather than simply “agentic” opens up useful design questions.

When you frame it as delegation, you can look to existing delegation relationships for guidance. What makes someone comfortable delegating their tax filing to a CPA? What trust factors matter when hiring an immigration attorney? These aren’t abstract questions — there are decades of real-world answers.

The language of delegation also centers the citizen experience more clearly. It’s not about what the AI can do autonomously; it’s about what citizens are willing to hand over and under what conditions. That subtle shift in framing can lead to different design choices around transparency, control and oversight.

Moving Forward

This isn’t a call to abandon the term “agentic services” entirely. It’s widely used in industry, and there’s value in using common language when talking with technology partners and vendors.

But maybe for internal discussions, policy development, and especially citizen-facing communications, it might be worth experimenting with terms like “delegation-based services” or similar language. It acknowledges continuity with existing practices, avoids potentially problematic associations with “government agents,” and keeps the focus on what citizens are actually doing: choosing to delegate burdensome tasks while maintaining appropriate oversight and accountability.

The technology may be new, but the underlying service delivery paradigm isn’t. Our language should reflect that.

Note – this post originally appeared on GovLoop.

#agent #AI #artificialIntelligence #ChatGPT #serviceDelivery

Revisiting an Old Idea: Building a Rules Engine with CouchDB

A few years back, while working at 18F, I created a prototype that explored something a bit unconventional: using CouchDB’s document validation functions as the foundation for a rules engine. The idea was to leverage CouchDB’s built-in validation capabilities to create business rules that could be applied to documents as they’re inserted or updated.

I’ve always been somewhat obsessed with CouchDB—there’s something elegant about its document-oriented approach and the way it handles replication, versioning, and distributed architectures. (Here’s a video I made over 10 years ago showing how to load polling location data into a CouchDB instance.) So even though my prototype remained just that, the concept has continued to bubble in the back of my brain.

Recently, I decided to dust off this old project and give it the attention it deserves. I’ve worked to develop a comprehensive roadmap to transform the basic prototype into a more functional and usable product that truly leverages CouchDB’s unique strengths.

What Makes This Interesting

Instead of building yet another traditional rules engine, this approach uses CouchDB’s native validation functions as the rule execution environment. This means:

• Native versioning through CouchDB’s document revision system
• Built-in replication for distributing rules across environments
• RESTful rule management using CouchDB’s HTTP API
• Distributed validation that scales with CouchDB clusters

The roadmap I’ve created takes my earlier work from a proof-of-concept to a (hopefully) production-ready system with a web-based rule management interface, comprehensive testing infrastructure, and advanced rule capabilities—all while maintaining the elegance of the core CouchDB foundation.

Looking Ahead

Over the next few weeks (again, hopefully), I’ll be working through the development phases, starting with a modern testing framework and a clean web interface for rule management. The goal is to create something that demonstrates how CouchDB’s unique features can be leveraged in ways that traditional databases simply can’t match.

If you’re interested in following along or have thoughts about creative uses for CouchDB, I’d love to hear from you. Sometimes the most interesting solutions come from pushing familiar tools in unexpected directions.

#art #books #CouchDB #governmet #Javascript#OpenSource #politics #rules #serviceDelivery #software #technology