{"id":139047,"date":"2026-08-13T18:11:44","date_gmt":"2026-08-13T18:11:44","guid":{"rendered":"https:\/\/www.europesays.com\/ai\/139047\/"},"modified":"2026-08-13T18:11:44","modified_gmt":"2026-08-13T18:11:44","slug":"the-agent-debate-is-asking-the-wrong-question","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/ai\/139047\/","title":{"rendered":"The Agent Debate Is Asking the Wrong Question"},"content":{"rendered":"

The conversation about AI agents in the enterprise has split into two camps, and both of them are wrong. One camp says agents are failing. The pilots looked good in the boardroom demo, went nowhere in production, and the whole thing is being oversold.<\/p>\n

The other camp says transformation is imminent, that agents will automate knowledge work at scale, and organizations that aren\u2019t moving aggressively are already falling behind. Neither camp can answer the question that actually matters: which specific workflows should I deploy an agent against, and why?<\/p>\n

That gap is showing up in the data. A March 2026 survey<\/a> of 650 enterprise technology leaders found 78% have at least one agent pilot running. Only 14% have successfully scaled to production. That is not a technology problem. The models are capable. The tooling has improved. The variable isn\u2019t the technology. It\u2019s workflow selection, and that\u2019s a problem no one is talking about clearly enough.<\/p>\n

The Agents that Work are Boring on Purpose<\/p>\n

The deployments that hold up in production share a consistent profile: narrow scope, repetitive execution, fully documented process, no judgment required. Just a sequence to complete. Think about the kind of call that floods a healthcare operations center: a physician\u2019s office calling to ask why a claims payment receipt didn\u2019t arrive, even though the payment actually went through. The answer involves pulling structured data from a defined set of systems and returning it in a predictable format. No ambiguity. No reasoning required. That\u2019s agent-ready.<\/p>\n

\"Get<\/p>\n

What\u2019s not agent-ready is any workflow that requires judgment calls an enterprise hasn\u2019t actually mapped yet. The failure pattern I keep seeing is organizations pointing agents at complex, ambiguous processes because those are the ones they most want to automate. Those are also exactly the ones agents are least equipped to handle. The harder a workflow looks to automate manually, the harder it is for an agent to do reliably. The irony is that the processes most worth automating first are the ones that feel too simple to bother with. High volume, low variance, fully legible process. Start there.<\/p>\n

Building Agents One at a Time is Not An AI Strategy<\/p>\n

At Availity, we process billions of healthcare transactions. We\u2019ve been featured in three AWS case studies and Amazon Q Developer now generates roughly a third of all code written by our engineers who use it. Hundreds of thousands of lines of AI-generated production code, running in a regulated environment. We know what it takes to get this out of the demo stage. What I\u2019ve watched happen across the industry is enterprises treating agents like projects: one team, one use case, one framework, one deployment. Ship it, move on, build the next one. Eighteen months in, they have fifteen agents, twelve frameworks, zero shared governance, and an audit committee that has no idea what any of it is doing.<\/p>\n

The insight I keep coming back to, reinforced by a recent conversation<\/a> with Anton Kornienko of NLP Logix who runs production-grade agentic AI: agents are not applications. They are stateful, autonomous, consequential. They don\u2019t sit still between requests. They plan, they act, and they produce side effects in systems that log things for regulators. Treating them like applications is how you accumulate risk faster than capability. The companies getting this right are not the ones with the best individual agents. They\u2019re the ones that built the platform to run any agent safely. The agent is not the product. The platform is the product.<\/p>\n

That platform has non-negotiables: policy enforced at runtime rather than reviewed in a committee; validation and control loops built into execution, not bolted on after; observability across decisions, actions, and outcomes; lifecycle management with the same rigor you\u2019d apply to any production asset; and shared infrastructure so the tenth agent costs a fraction of the first. None of that ships with a demo. All of it is the difference between a pilot that impresses a boardroom and a deployment that survives regulatory contact.<\/p>\n

The Risk that Doesn\u2019t Fail Loudly<\/p>\n

When AI-generated output accumulates faster than human review can catch up, you have a new category of operational risk. In a regulated environment like healthcare IT, that means audit exposure, compliance gaps, and errors embedded in systems that are hard to untangle after the fact.<\/p>\n

The organizations most at risk are not the ones moving too fast. They\u2019re the ones moving fast in the wrong direction. An agent deployed against the wrong workflow, without the infrastructure to observe and govern it, doesn\u2019t fail loudly. It fails quietly, over time, in ways that are hard to detect until they become expensive to fix.<\/p>\n

This is also why the \u201cwait for governance\u201d posture I see from some enterprise leadership teams doesn\u2019t protect you. It just shifts who owns the problem. Somewhere in your organization, an engineer with a problem and a laptop is already running agents. The governance question isn\u2019t whether agents are running. It\u2019s whether you have any visibility into what they\u2019re doing.<\/p>\n

How The Standoff Breaks<\/p>\n

The practical path forward is less dramatic than either camp suggests. Start with local agents, work automation within the security envelope your engineers already operate in. Agents refactoring code, writing tests, searching logs, drafting documentation. The same human owns every outcome. The risk profile is closer to a smarter IDE than to an autonomous service.<\/p>\n

That move doesn\u2019t require organizational approval, committee sign-off, or a new governance framework. It produces real usage patterns, real risk surface data, and credibility you spend later when you do need approval for the centralized platform. At Availity, we had engineers demo the agentic workflows they built internally at town halls, from managing tickets to running full workflows locally on their workstations. The kind of demos where the room leans forward. That\u2019s how you earn the platform conversation: with working code instead of strategy slides.<\/p>\n

The debate about whether agents are delivering or failing is not the right question for enterprise leaders to be spending time on. The right question is more specific: have you mapped the workflows in your organization that are narrow enough, repetitive enough, and well-documented enough to be agent-ready right now? If you haven\u2019t done that work, you don\u2019t yet have the information you need to deploy successfully or to evaluate whether the technology is working.<\/p>\n

The gap between 78% running pilots and 14% reaching production is not a story about AI. It\u2019s a story about workflow selection and platform readiness. Fix that, and the technology takes care of itself.<\/p>\n

\"MichaelMichael Privat<\/a> is Chief Data and Engineering Officer at Availity, the nation\u2019s largest health information network. He has 25 years of experience in healthcare IT and writes about AI, engineering leadership, and organizational change on Substack<\/a>.<\/p>\n

\u00a0<\/p>\n

\u00a0<\/p>\n

Related stories:<\/p>\n

AI Agents Revolutionized B2B Marketing in 2025<\/a>
\nTypeface\u2019s Satya Krishnaswamy on Why AI Agents Stall Before They Scale: The Demand Gen Report Q&A<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"The conversation about AI agents in the enterprise has split into two camps, and both of them are…\n","protected":false},"author":2,"featured_media":139048,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6],"tags":[405,7537,426,523,428,29602,68598,32365,22593,7895,68599],"class_list":["post-139047","post","type-post","status-publish","format-standard","has-post-thumbnail","category-agentic-ai","tag-ai-agents","tag-artificial-intelligence-agents","tag-autonomous-systems","tag-enterprise-ai","tag-governance","tag-healthcare-it","tag-operational-risk","tag-platform-strategy","tag-production-ai","tag-workflow-automation","tag-workflow-selection"],"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/139047","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/comments?post=139047"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/139047\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media\/139048"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media?parent=139047"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/categories?post=139047"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/tags?post=139047"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}