According to Okta's CEO, the most valuable application for AI agents in the enterprise will be orchestrating complex processes that span multiple software silos (e.g., Salesforce, SAP, Content Management). This is a task that has historically been difficult to automate with packaged software and required human intervention, representing a massive new opportunity.
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The evolution of 'agentic AI' extends beyond content generation to automating the connective tissue of business operations. Its future value is in initiating workflows that span departments, such as kickstarting creative briefs for marketing, creating product backlogs from feedback, and generating service tickets, streamlining operational handoffs.
OpenAI's new platform, Frontier, is designed for building 'AI co-workers' that can access a company's various data sources and systems. This represents a strategic move beyond single-user chatbots toward an enterprise-grade orchestration layer for managing teams of interconnected AI agents.
Enterprises are trapped by decades of undocumented code. Rather than ripping and replacing, agentic AI can analyze and understand these complex systems. This enables redesign from the inside out and modernizes the core of the business, bridging the gap between business and IT.
Infrastructure built for app-to-app integration, like Salesforce's MuleSoft, is being repurposed to govern, orchestrate, and secure AI agents. This 'agent fabric' provides a foundational control plane for managing complex agentic workflows across the enterprise, extending the value of existing integration investments.
The durable investment opportunities in agentic AI tooling fall into three categories that will persist across model generations. These are: 1) connecting agents to data for better context, 2) orchestrating and coordinating parallel agents, and 3) providing observability and monitoring to debug inevitable failures.
Legacy systems like CRMs will lose their central role. A new, dynamic 'agent layer' will sit above them, interpreting user intent and executing tasks across multiple tools. This layer, which collapses the distance between intent and action, will become the primary place where work gets done.
Instead of interacting with SaaS GUIs (like Greenhouse for hiring), users will interact with AI agents. These agents will directly manipulate the underlying system-of-record data, managing entire workflows from a simple conversation and making the traditional SaaS application redundant.
Traditional SaaS was built for siloed human departments (e.g., sales, marketing, support). AI enables a single agent to manage the entire customer journey, forcing these distinct software categories to converge into unified platforms.
The next evolution of enterprise AI isn't conversational chatbots but "agentic" systems that act as augmented digital labor. These agents perform complex, multi-step tasks from natural language commands, such as creating a training quiz from a 700-page technical document.
The current market of specialized AI agents for narrow tasks, like specific sales versus support conversations, will not last. The industry is moving towards singular agents or orchestration layers that manage the entire customer lifecycle, threatening the viability of siloed, single-purpose startups.
