The AI agent infrastructure market is exploding — LangChain just raised $125 million; hyperscalers and SaaS vendors are spinning up agent frameworks and control towers; MCP and A2A protocols are converging as standards; and observability vendors are racing to add LLM tracing.

And enterprise architecture (EA) tools vendors? You’re still talking about application rationalization. This is a strategic blind spot that will cost you market position — or hand you a category-defining opportunity. Here’s why.

The Industry Has An AI Design-Time Problem

Watch where the money and attention are flowing in agentic AI: runtime orchestration, production observability, prompt optimization, and model evaluation. LangChain’s newly coined “agent engineering” discipline is explicitly about making agents work reliably after they’re deployed.

But here’s what nobody is systematically addressing: the scoping and design decisions that should happen before anyone writes a prompt. Where–across the landscape of organizational value streams and capabilities–do we think AI can create business value? When should a workflow be agentic vs. traditional automation? What semantic definitions must agents share to interoperate? How do agents compose with existing application portfolios? What authorization boundaries apply? What’s the token cost projection? Which business processes have the error tolerance for probabilistic outputs?

These are hard questions. And right now, enterprises are answering them through tribal knowledge, scattered spreadsheets, and expensive mistakes in production.

Why Your Metamodel Is The Answer

EA tools are the only software category with metamodels broad enough to span the dimensions that AI design-time decisions actually require:

  • Business architecture determines where autonomous and less deterministic decision-making fits organizational operating models, processes, and capabilities.
  • Data architecture establishes semantic alignment between agents and enterprise systems.
  • Application architecture maps existing workloads and APIs to capabilities, workflows, business rule automation, and composition patterns.
  • Technology architecture assesses infrastructure requirements and constraints.
  • Security and risk defines authorization boundaries and guardrail requirements.

And you will need to add a new emerging element: Skills, Product & Workforce architecture. Agents aren’t employees but they do represent the software product equivalent of human cognitive capabilities. In the past these were the sole purview of Human Resources and oftentimes ignored by EA and EA tools. But the curation and refinement of knowledge and delivery of this in the form of skills, roles and workflows to augment, replace and rationalize people means EAs must now work with HR to outline how enterprises workforces will transform.

No runtime framework touches all of the above. Observability platforms don’t model business capability alignment. Agentic control towers don’t (yet) understands application portfolio. You already have the architectural breadth. What you’re missing is the AI-specific vocabulary and use cases to activate it.

What “AI-Ready” EA Tools Would Look Like

Imagine your platform with:

  • AI scoping frameworks embedded in capability models — decision trees for when agentic approaches are appropriate, with risk and readiness scoring.
  • Agent registry as a first-class portfolio citizen, with autonomy levels, behavioral constraints, and semantic dependencies mapped.
  • Semantic alignment workbenches (probably aligned with ontology design tools) that surface potential conflicts before agents reach production — what I’m calling “semantic observability” at design time.
  • FinOps/ITFM integration to project token costs against business value, tied to your existing IT financial management views.
  • Risk tagging that flows AI-specific concerns into enterprise risk frameworks your clients already operate.

This isn’t a feature list — it’s a category repositioning: from “EA tools” to “AI governance platforms.”

The Window Is Open — For Now

Your competition isn’t other EA vendors — it’s the emerging AI governance startups that will build purpose-specific tools and claim this territory while you’re still demoing application dependency maps. Worse, it’s the platform engineering tools adding lightweight architecture views that are “good enough” for AI-specific decisions — fragmenting the design-time landscape the same way shadow IT fragmented application portfolios a decade ago.

The enterprises deploying agents at scale in 2026 will need design-time governance. They’ll need to answer “Should this be agentic?” before they answer “How do we make this agent reliable?” Someone will provide that platform.

EA management suites are now a $1 billion market, and the future has never looked brighter for EA. Your metamodel already spans the problem space; the question is whether you’ll recognize the opportunity before the market builds around you.