Perplexity is positioning itself at the frontier of a fundamental shift in how people interact with AI. Rather than stopping at providing answers, the company is now focused on building what it calls the "action engine," a next-generation interface where AI agents can perform real work on behalf of users. This evolution represents a natural progression from the traditional search engine to the answer engine, and now to autonomous task execution.

The vision was articulated by Ryan Foutty, Vice President of Business at Perplexity, during remarks at GITEX AI EUROPE 2026, a major tech conference bringing together global leaders in Berlin from June 30 to July 1. Foutty emphasized that search has remained largely unchanged for three decades, and when Perplexity first proposed transforming the search engine into an answer engine, the idea was widely unpopular. Now, he explained, the company sees the next logical step.

"Search has been largely unchanged for the last 30 years. When we set out to turn the search engine into the answer engine, it was widely unpopular. Now, people see the vision we had. The next step and natural evolution of the answer engine is the action engine, which is an interface where agents can do real work on your behalf," said Foutty.

Ryan Foutty, Vice President of Business at Perplexity

Perplexity, valued at $20 billion and backed by major investors including NVIDIA, Jeff Bezos, and AI researcher Yann LeCun, is not alone in recognizing this shift. The broader enterprise software industry is grappling with how to operationalize AI beyond information retrieval. Optimizely, a leading digital experience platform, recently launched a comprehensive Answer Engine Optimization (AEO) platform that includes autonomous agents designed to act on marketing data without human intervention.

What Does an "Action Engine" Actually Do?

An action engine represents a departure from passive information consumption. Instead of asking an AI assistant a question and receiving a synthesized answer, users would delegate tasks to AI agents that can execute decisions, make changes, and take steps on their behalf. This could range from automating content optimization decisions to executing business processes that currently require human oversight.

The practical implications are significant. In the marketing technology space, companies like Optimizely are already deploying autonomous agents that perform specific functions without waiting for human approval. Three new agents were announced as part of Optimizely's launch:

• AEO Gap Finding Agent: Identifies high-priority topics where a brand is losing to competitors in AI search results, delivering a prioritized plan to close those gaps.
• Competitive AI Share of Voice Agent: Provides automated benchmarking of a brand's visibility in AI answers against key competitors across different topics.
• AI Brand Visibility Report Agent: Automatically generates comprehensive reports on brand ranking, share of voice, and sentiment within major AI engines, complete with actionable next steps.

These agents represent a broader industry trend toward what analysts call "agentic orchestration," the ability for AI systems to not just present data but to intelligently and autonomously execute tasks based on that data.

How Enterprises Can Prepare for the Action Engine Era?

For enterprises looking to move beyond pilot programs and make AI the default interface for how work gets done, several practical steps are emerging from the market. The transition from answer engines to action engines requires both technical infrastructure and organizational readiness.

• Establish Clear Data Foundations: Enterprises need factual, log-level visibility into how AI agents interact with their systems and content. Optimizely's Agent Visibility Analytics, for example, taps directly into server logs to provide a factual record of which AI agents are visiting sites, what content they access, and for what purpose.
• Integrate Market Intelligence with Internal Data: Understanding what AI agents are doing on your domain is only half the battle. Combining that internal visibility with broader market context, such as what competitors are doing and what real users are searching for, enables smarter autonomous decisions.
• Design Agents for Specific Business Outcomes: Rather than deploying generic AI agents, enterprises should build or configure agents designed to act on specific business priorities. This requires clarity on which decisions can be safely automated and which require human oversight.

Why Does This Matter Now?

The timing of this shift is not coincidental. The rise of AI answer engines like ChatGPT, Claude, and Google's AI Overviews has already disrupted how information is discovered and consumed. Industry analysts are forecasting a 25 percent drop in traffic from traditional search engines by 2026 as users increasingly turn to AI assistants for direct, synthesized responses.

For enterprises, the challenge is no longer just about appearing in search results. The new currency of visibility is citation, and the critical questions for business leaders have become: Is our brand being mentioned in AI-generated answers? Is the information accurate? And what content are the AI's retrieval agents actually consuming from our websites?.

Perplexity's focus on the action engine suggests the company believes the next competitive advantage lies not in better answers, but in better execution. By enabling AI agents to take action on behalf of users, Perplexity is positioning itself at the center of how enterprises will interact with AI in the coming years. For organizations that move early, the potential to automate decision-making and task execution at scale could represent a significant competitive advantage.