OpenAI's AgentKit Brings Enterprise AI Agents to Smaller Businesses
What McKinsey spent months and millions building, Ramp built in hours.
That's the shift happening right now. AI agent capabilities that required enterprise budgets and dedicated engineering teams just became accessible at standard API pricing.
OpenAI launched AgentKit last week, arriving as AI agents are proving their value. Clay saw 10x growth with a sales agent. Lumen cut sales prep from 4 hours to 15 minutes, projecting $50 million in annual savings. McKinsey deployed 12,000 internal agents to support consultants.
The pattern is clear: AI agents work. What's changing is who can build them.
AgentKit provides everything you need to build, deploy, and optimize AI agents: visual workflow builders, embeddable chat interfaces, performance measurement tools, enterprise data connectors. The barriers that kept AI agent development expensive and slow just collapsed.
In This Article...
What AgentKit Actually Includes
AgentKit bundles four core capabilities that address specific friction points in building agents.
Agent Builder provides a drag-and-drop canvas for composing workflows. Your non-technical team members can build, test, and modify agents without needing engineering resources. The interface looks like Canva for AI.
Ramp went from concept to deployed buyer agent in hours, achieving a 70% reduction in iteration cycles. Their legal, product, and engineering teams collaborated directly without needing translators between departments.
ChatKit handles the complexity of deploying chat interfaces: streaming responses, thread management, model thinking display. You can customize the interfaces and embed them so they feel native to your product.
Evals provides tools for measuring agent performance: datasets for testing, trace grading for end-to-end assessment, automated prompt optimization, third-party model support. You can measure and improve performance before your clients interact with agents.
Connector Registry consolidates data sources into a single admin panel with pre-built connectors for Dropbox, Google Drive, SharePoint, Microsoft Teams, plus third-party MCP support. Your agents can access client history, project documentation, and industry research automatically.
What AI Agents Are Already Accomplishing
Before AgentKit's launch, companies were already deploying AI agents with substantial results. These examples show what becomes possible when the right functions get automated.
Sales & Lead Generation
Clay deployed a sales agent and saw 10x growth. The data enrichment platform built something that fundamentally changed their trajectory without needing AI engineers or months of development.
Lumen cut sales prep from 4 hours to 15 minutes per prospect. Sellers used to spend extensive time summarizing past interactions, pulling recent news, identifying business challenges, and tracking industry trends. AI agents now handle this work. The company projects $50 million in annual time savings, with freed hours going directly into higher-value client interactions.
Client Support & Service Delivery
Klarna built a support agent handling two-thirds of all tickets, scaling client service without proportional team growth. The company can expand its client base without hiring and training support staff at the same rate.
HubSpot deployed a customer support agent with chat-based interfaces providing always-on availability. Clients get immediate responses during off-hours while the support team focuses on complex issues requiring human judgment.
Operations & Internal Efficiency
Ramp went from concept to deployed buyer agent in hours, launching projects in two sprints instead of two quarters. The visual workflow approach meant cross-functional collaboration without the traditional back-and-forth of requirements documents.
McKinsey deployed approximately 12,000 AI agents internally to support consultants, enabling leaner project teams. The agents handle research, analysis, and documentation that previously required junior consultants, fundamentally changing project delivery economics.
The Pattern
AI agents handle analytical and administrative work that used to require either your time or expensive team members. Client support, research, qualification, documentation. The tasks that scale linearly with revenue growth.
Your expertise remains irreplaceable. What's changing is the infrastructure around that expertise.
Considerations for High-Ticket Businesses
AgentKit makes AI agent development accessible. The practical question is where you should start and what to avoid.
Where to Start
Look at tasks you repeated across multiple clients last month: client onboarding conversations covering the same ground, research projects following similar patterns, documentation requiring the same information gathering process.
Map where your team spends time on analytical versus strategic work. Analytical work following clear patterns and defined processes typically makes strong candidates for agent automation.
What Works Well
Agents excel at routine, low-risk tasks: client intake and qualification, research and competitive analysis, meeting preparation and summarization, document organization and retrieval, progress tracking and reporting.
These tasks consume significant time but don't require your specific expertise or judgment.
What Still Requires You
Agents struggle with high-stakes strategic work: nuanced client relationship building, complex problem solving requiring extensive context, strategic recommendations and positioning, high-stakes negotiations and decisions, creative and interpretive work.
Deploy agents for the analytical infrastructure that supports your expertise, not for the expertise itself.
The Reliability Question
Tools like AgentKit include evaluation capabilities for measuring accuracy. You can test agents on representative scenarios before client-facing deployment. Start with internal use cases to build confidence. Scale to client-facing applications as reliability proves out.
The companies seeing results didn't deploy agents blindly. They tested, measured, and refined before expanding to client-facing applications.
Cost Structure Reality
AgentKit uses standard API pricing with no enterprise premiums. If you're a high-ticket consultant billing $300 per hour and you save 2 hours weekly on research, that represents $31,200 in annual value. Most implementations cost a fraction of this in API fees, with positive ROI usually appearing within the first month.
Technical Requirements
You don't need coding for basic agent building with tools like Agent Builder's visual interface. Start with pre-built templates and connectors. API integration becomes necessary for custom implementations. Most businesses start simple and add sophistication as they understand what works.
Your Next Decision
The question isn't whether AI agents work. The examples from Clay, Lumen, McKinsey, Klarna, and others prove the value.
Your question is which business functions to automate first. How to maintain premium positioning while leveraging automation. When to move from testing to client-facing deployment.
AgentKit removed the technical and financial barriers. What used to require enterprise budgets and engineering teams now requires strategic thinking about which tasks to automate and how to deploy the saved capacity toward higher-value work.