OpenAI just launched Frontier. It's exactly what enterprises have been waiting for: a platform that finally makes AI agents useful inside real business workflows. Agents get shared context across your systems. They get execution environments to actually do work. They get governance and permissions so IT doesn't panic.
But there's a gap in this picture. A big one.
When an agent needs to interact with a human, what happens? When it needs to show you something, collect your input, or guide you through a decision? Right now, the answer is a wall of text in a chat window.
What Is OpenAI Frontier?
OpenAI Frontier is an enterprise platform for deploying AI agents that can operate inside your business, not just talk about it. The platform launched February 5, 2026, with early customers including Intuit, Uber, State Farm, and Thermo Fisher and AI builders like Clay, Sierra, Decagon, and Harvey.
Frontier has three core components.
Shared business context. Frontier connects your data warehouses, CRMs, ticketing tools, and internal applications. This gives agents the same institutional knowledge your employees have. OpenAI calls it "a semantic layer for the enterprise that all AI coworkers can reference."
Agent execution. Agents can plan, reason, work with files, run code, and use tools across real workflows. They don't just answer questions. They complete tasks.
Enterprise governance. Identity management, explicit permissions, and audit trails for every agent action. Agents operate under the same controls as human employees.
Fidji Simo, OpenAI's CEO of Applications, described Frontier as the solution to fragmented AI tooling. At Instacart, she dealt with dozens of vendors, months of integration work, and tools that couldn't talk to each other. Frontier is designed to consolidate all of that into one platform.
The pitch is compelling. Agents that understand how your business works. Agents that execute tasks, not just suggest them. Agents that are safe to deploy at scale.
But here's what Frontier doesn't solve.
What Happens When an Agent Needs to Talk to a Human?
The interaction layer is still just chat.
When your Frontier-powered agent finishes analyzing your sales pipeline and needs to show you the results, it types out a description. When it needs your approval on a purchase order, it asks in plain text. When it needs to collect structured information from you, it's playing 20 questions in a chat window.
Your agent might have perfect context about your Q4 revenue targets, your team's capacity, and the three deals most likely to close this month. But the way it communicates all of that to you? Paragraphs. Bullet points if you're lucky.
This is the text wall problem. The agent is smart. The experience of using it is not.
Why Chat Isn't Enough for Enterprise Workflows
Consider what real enterprise work actually looks like.
A sales manager needs to review pipeline health across 40 accounts, compare stage progression week-over-week, and flag the deals that need attention. In chat, the agent describes each account sequentially. With a proper interface, you see a sortable dashboard with filters, drill-downs, and visual indicators.
A finance lead needs to approve a $50,000 vendor contract. The approval requires confirming budget allocation, selecting a cost center, attaching supporting documents, and adding conditions. In chat, the agent asks each question one at a time and hopes you don't make a typo. With a proper interface, you see a structured form with validation, dropdowns, file upload fields, and a clear submit action.
An HR coordinator needs to onboard a new employee through benefits enrollment, equipment requests, and policy acknowledgments. In chat, this becomes a tedious back-and-forth that takes an hour. With a proper interface, it's a guided workflow the new hire completes in fifteen minutes.
The pattern is clear. Agents can understand complex business contexts and execute sophisticated tasks. But the moment they need human input or need to present information for human decisions, they hit a ceiling.
That ceiling is the chat interface.
Apps Are the Interface Between Agents and Humans
When an agent needs to show, collect, or guide, that's an app.
Not an app in the traditional sense. An AI-native app is a structured interface that lives inside the AI platform itself, designed specifically for agent-human interaction.
When the agent needs to visualize data, that's an app rendering a dashboard. When the agent needs to collect a decision with specific constraints, that's an app presenting a form with validation. When the agent needs to walk someone through a multi-step process, that's an app managing a workflow with state and progression.
These interfaces turn agent intelligence into agent usability. They're the difference between "the agent knows everything about your business" and "the agent is actually pleasant to work with."
Every other software category figured this out decades ago. We don't interact with databases by typing SQL queries in a terminal. We use applications with tables, filters, and visualizations. We don't manage projects by writing plain text descriptions of tasks. We use interfaces with boards, timelines, and status indicators.
AI agents are the most capable software we've ever built. And they're stuck in text-only mode.
The Enterprise AI Stack Now Has Two Layers
Frontier represents the maturation of the agent backend. It's the infrastructure for making agents smart about your business, capable of executing real work, and safe to deploy at scale. Context, execution, governance. The hard problems enterprises have been stuck on for two years.
The missing piece is the agent frontend. The infrastructure for making agents usable by humans. Interfaces, workflows, cross-platform deployment. The experience layer that sits on top of all that backend intelligence.
Agent backend (Frontier): How agents understand your business. How they execute tasks. How they're governed and secured. The brain.
Agent frontend: How agents present information to humans. How they collect input and decisions. How they guide users through workflows. The face.
Every company deploying Frontier needs both layers. You can't have agents that are smart but unusable.
This Gap Will Only Get Wider
The agent backend is improving fast. Frontier is just the beginning. Anthropic has Claude for Work and enterprise MCP integrations. Google is pushing Gemini into enterprise workflows. The underlying models are getting more capable every quarter.
As agents get smarter, the interactions they need to have with humans get more complex. A basic Q&A agent can survive on chat. An agent managing your entire procurement workflow needs real interfaces.
The more Frontier succeeds, the more agents get deployed, the more workflows they touch, and the more deeply they integrate into business operations. And the more acute the interface problem becomes.
Layo: The Agent Frontend Platform
Layo is the platform for building AI-native apps. It's the frontend layer for the enterprise AI stack.
Product teams use Layo to build structured interfaces that run directly inside ChatGPT, Claude, and Gemini. Dashboards. Forms. Workflows. Interactive components. Design once, deploy across every major AI platform.
Here's how it works. You build your interface visually in Layo. You connect it to your business context. You publish it as an app that agents can invoke when they need to interact with humans.
When the sales agent needs to show a pipeline dashboard, it pulls up the Layo app. When the procurement agent needs approval on a PO, it presents the Layo form. When the HR agent needs to onboard someone, it launches the Layo workflow.
This closes the loop on what Frontier promises. Frontier gives your agents the intelligence to understand a complex approval workflow. Layo gives them the interface to actually execute it with a human in the loop.
Frontier makes your agents smart. Layo makes them usable.
For product teams, the calculus on agent deployment just changed. You're no longer choosing between "accept chat limitations" and "build everything custom." You're building real interfaces at the speed enterprises need to move, and you're shipping them across every AI platform your organization uses.
Frontier just created massive demand for agent interfaces. Layo is built to meet it.