From Passive Tools to Active Employees: The Three Major Changes of AI Agents in 2026

a16z’s latest investment insights point out that artificial intelligence is undergoing a fundamental transformation—evolving from a passive “response machine” to an active “digital employee.” This not only changes the technological form but, more importantly, opens up a market space 30 times larger.

The End of Input Boxes: The Revolution in AI Application Interaction Modes

Marc Andrusko, head of a16z’s AI application investment team, made a bold prediction—that by 2026, traditional input box interfaces will gradually disappear.

This means users will no longer need to carefully craft complex command texts. Next-generation AI applications will automatically observe user behavior, proactively identify needs, suggest solutions in advance, and wait for user confirmation of the final step. This paradigm shift unlocks enormous business opportunities.

Market Scale Leap

The real reason investors are excited is the expansion of the target market. The global annual expenditure on traditional software is about $300-400 billion, while labor costs reach $13 trillion (just in the US). This implies a potential market opportunity that has expanded 30-fold—from hundreds of billions to trillions.

From an employee capability model, this change aligns with the work pattern of “top-tier S-level employees”: they do not passively wait for instructions but actively identify problems, diagnose root causes, research multiple feasible solutions, execute the optimal one, and finally report to decision-makers with “Please approve my plan.” This is the ultimate form of AI application.

Andrusko uses CRM applications as an example: current salespeople need to manually open systems, scan opportunities, check schedules, and then think about how to maximize funnel conversion. AI CRM assistants should continuously perform these operations for sales agents—not only identifying recent opportunities but also reviewing emails from two years ago, discovering cold potential clients, and proactively suggesting reactivation strategies.

Designing for Machines, Not Humans: New Logic for Content and Software

Stephanie Zhang, partner at a16z Growth Investments, pointed out a deeper paradigm shift in design—products are no longer built for human eyes but optimized for the “understanding” of agents.

From Visual Hierarchy to Machine Readability

In the human-first era, content creation followed the “5W1H” journalistic principles—capturing attention at the start. Designers carefully structured visual information hierarchies, aiming for each button to be intuitive and easy to use. But these optimization principles are becoming outdated in the agent era.

The current scene is already changing: when a server fails, engineers need to open Grafana dashboards and troubleshoot item by item. In the future, AI SRE assistants will automatically collect all telemetry data, analyze the entire stack, and send diagnostic hypotheses directly in Slack—organizing data in the most machine-friendly way, without visual beautification.

Sales teams used to need to click through Salesforce to gather CRM information; now, agents can directly extract structured data and send insights summaries to sales representatives.

The Emergence of “Generative SEO”

This shift has led to unexpected consequences— the internet is beginning to be flooded with content optimized for agents. Zhang observed that many tools have already appeared to help organizations prioritize their products when ChatGPT is asked “Best business credit card.” This is similar to keyword stuffing in the SEO era, but the target audience is algorithms, not humans.

Companies are generating大量 low-quality but highly targeted content for agents. Since AI models can read entire articles (while humans typically only scan the beginning), the creation cost is nearly zero, which could lead to a vast amount of “agent-friendly garbage content” online.

In the case of portfolio company Dekagon, AI can already automatically generate responses for many clients. But in high-risk areas like security operations or incident response, humans still need to remain involved in decision-making—agents provide multiple possible solutions, and humans make the final confirmation.

The Industrial Turning Point of Voice Agents

Olivia Moore, partner at a16z AI application investments, pointed out that 2026 will mark the transition of voice AI from the conceptual stage to full-scale commercial deployment.

From Trials to Deployment: Comprehensive Application Coverage

By 2025, voice agents have shifted from “future technology” to real systems adopted by enterprises at scale. Almost every major vertical industry has clients testing or deploying voice AI solutions.

Healthcare has become the largest application area. Voice AI has penetrated the entire care process: insurance calls, pharmacy coordination, communication with medical suppliers, and even sensitive scenarios on the patient side—such as post-surgery follow-up calls and initial mental health assessments—are handled by AI voice systems. The core driver of this application is the high turnover and recruitment difficulties faced by the healthcare industry, making reliable voice agents a feasible solution to address labor shortages.

Compliance Advantages: AI Outperforms Humans

Adoption in financial services is also rapid, despite strict regulation. In fact, this is where voice AI performs best—because humans are adept at avoiding regulations, while AI voice systems can strictly follow every rule, with all actions fully traceable and auditable.

Recruitment processes are also being transformed by AI voice. From frontline retail roles to entry-level engineering positions and even mid-level consulting roles, AI can create 24/7 interview experiences, automatically guiding candidates into subsequent recruitment stages.

Differentiation in BPO and Call Centers

Currently, in some regions, labor costs are still lower than top-tier voice AI systems. But as model performance improves, this cost gap is narrowing. Moore pointed out that although companies may continue to outsource services (rather than build their own technology) in the short term, they will prioritize lower-cost or higher-volume providers—those already integrated with AI capabilities.

This creates a risk of segmentation for traditional BPOs and call centers: operators who can effectively integrate AI may transition smoothly, while those lacking technological adaptation face a “deep abyss” threat. As Moore states, “AI won’t take your job, but people who know how to use AI will.”

Government agencies are the next frontier. a16z-backed startup Prepared is already handling non-emergency 911 calls. In the future, similar systems could handle DMV calls and other government hotlines—interactions that today cause frustration for both consumers and staff.

Multilingual and Accent-Robust Performance

Voice AI performs excellently in multilingual conversations and heavy accents. Moore mentioned that words or phrases she couldn’t clearly understand in meetings are perfectly captured by voice transcription systems like Granola. This is a standard capability of current ASR and speech-to-text providers.

Interestingly, some companies even deliberately add delays or background noise to voice agents to make them sound more human, avoiding user discomfort.

Industry, Not Just Market

Moore emphasizes that voice AI should be viewed as a complete industry rather than a single market. Every layer of the tech stack offers opportunities for winners—from foundational models to platform-level applications—entrepreneurs can find entry points at any stage. She recommends that entrepreneurs start by experimenting with open-source platforms like 11 Labs to build voice agent prototypes and understand technological boundaries and possibilities.

Consumer-facing voice AI applications are still mainly in B2B. But the healthcare sector shows new consumer directions: voice AI companions are deployed in assisted living facilities and nursing homes, serving both as companions and continuously monitoring residents’ health metrics.

The Deep Logic Behind the Three Major Changes

The common thread among these three predictions is: AI is evolving from a human tool to an independent agent.

The first change is the disappearance of interaction interfaces—users no longer need carefully worded commands, greatly lowering the usage barrier. The second is a shift in design philosophy—products are no longer optimized for human visual and cognitive preferences but for algorithmic processing efficiency. The third is the maturation of application paradigms—from demonstration to large-scale operation, especially in industries with strict compliance and traceability requirements.

These changes will be fully evident by 2026, but the seeds have already sprouted in 2025. For entrepreneurs, the opportunity lies in understanding the competitive landscape at every stage—from better foundational models, to industry-specific agents, to solution integrators.