Research Objective: This assessment analyses whether AI agents could replace virtual assistants by 2026. AI agents are defined as any AI capable of carrying out intelligent tasks for a user or customer. Virtual assistants (VAs) are defined as interaction systems based on deep learning-natural language-based internal architectures capable of executing tasks on behalf of a user or customer. The probability of replacement is no greater than 10%. Augmentation or hybrid models are more likely outcomes.
Artificial Intelligence (AI) continues to advance toward systems that can execute complex tasks with little human assistance. VAs have supported users in executing basic tasks for nearly a decade. Recent advances in dialogue capabilities through the development of AI agents could evolve into products that replicate the key capabilities of VAs. Key competing hypotheses evaluate trajectories for product evolution toward the 2024–2026 timeframe. Three scenarios replacement, augmentation, and hybrid solutions consider utilitarian demand and technical capabilities needed for deployment in the 2024–2026 timeframe. Drivers of change such as data availability, compute and infrastructure costs, governance, user demand, and design methodology are weighed alongside potential limiting factors such as privacy, latency, and data availability.
Background: Virtual Assistants and AI Agent Technologies
Virtual assistants and AI agents increasingly deliver convenient and sophisticated interactive experiences across multiple channels supporting activities such as customer engagement and complex problem solving. Virtual assistants are architectural and system designs for implementing intelligent agents that provide a conversational interface to a specific set of applications or a narrower class of tasks and topics. Current virtual assistants rely on an assortment of AI-enabled tools, some still very primitive, to accomplish tasks. Task understanding, dialogue management, natural language understanding and generation, multi-modal perceptual capabilities, and planning may fall into the hands of separate processes that work together as a team rather than as a single agent.
Notably, recent advances in the capabilities of prompt-engineered AI agent capabilities for executing specific tasks are astounding: in a few cases they dramatically exceed the capabilities of virtual assistants much in the same way as Go-playing agents astonished the world when they beat human champions. Nevertheless, in most respects these AI agents remain in the early stages of development. Real-world usage demonstrates that a currently available AI agent can generate elegant and accurate responses, yet it is still fragile when tasked with actions across the long time horizons typical in human life. Leaps in the improvement of AI agents’ capabilities should continue over the next couple of years. What remains less clear is whether AI agents will replace virtual assistants, replace human labor, engage customers in even more profound ways, or ultimately become a hybrid solution.
Current Capabilities of AI Agents in 2024–2025
Assessing the Likelihood of AI Agents Replacing Virtual Assistants by 2026
What is the capability of current AI agents from various developers in 2024–2025?
Over the last two years, leading AI vendors have focused on developing specialized AI agents capable of performing specific tasks like generating text, summarizing content, creating images, or solving complex queries. These systems are becoming increasingly flexible. Key improvements include higher task completion rates, the ability to retain contexts beyond a single request, the capability to manage conversations across multiple turns, and integration with vertical applications.
Key capabilities available in various AI agent products, including AutoGPT and Function Calling combined with ChatGPT, Bard, and Claude, can be summarized as follows:
Execution of specific tasks: Automation of a single task or a set of related tasks with known inputs and outputs through interaction with appropriate AI technologies, knowledge bases, or data sources (for example, generating a song about a specific topic).
Context retention across multiple turns of a dialogue: Ability to remember information from previous turns to improve coherence and relevance of responses based on a predefined retention strategy.
Multi-turn dialogue capability: Execution of tasks requiring multiple turns of dialogue, including maintaining dialogue state, tracking information supplied by the user, and clarifying user preferences. Multi-turn capabilities also facilitate interactions requiring the user to switch between different facets.
Integration with external applications: Capability to invoke external services to implement specific functionalities, often through the use of plug-ins or other interoperable connectors that allow application data to be accessed by the AI agent in a secure manner.
After accounting for these capabilities, it is clear that currently available AI agents still do not possess the cognitive flexibility displayed in multi-faceted environments by a human being or a more general system, such as an AI assistant or agent.
Drivers of Change and Limiting Factors
Capabilities today and in 2024–2025 are only one piece of the forecasting puzzle. The underlying technologies must also develop further. Four categories of associated trends can help to accelerate that natural development: greater quantities and better quality of data; increased compute capacity, improved interoperability among ecosystem components; and increasing standardization of supporting technologies. In addition, two other categories can hinder progress: privacy or regulatory concerns; those that affect the longer-term quality of the technology itself, such as bias or unfairness in the algorithms; and challenges either in the technical conduct of the modelling or in the core data issues of the supervised machine learning.” These two groups form that effect of an increasing quality of the technology, discussion of recent real-world example application of AI agents.
Two further categories are environmental influences: regulation and data-driven demand for clear-cut proprietary or semi-proprietary functionality. Finally, in some applications, the development of corporate dependency on human undertakings may also be a natural brake on capacity change, whether because of stability considerations or a straightforward wish to be available to customers.
Scenarios for 2026: Replacement, Augmentation, and Hybrid Models
The future prospects of AI agents can be distilled into three scenarios that capture the key ways in which they could impact the availability and functionality of conventional virtual assistants. They are opposed in pairs replacement versus augmentation, and augmentation versus hybridization with the third option providing a middle ground.
In the replacement scenario, AI agents provide the same functions that virtual assistants currently offer but with greater reliability, flexibility, safety, and explainability. A shift in the balance of these quality attributes is required to make this outcome possible. Instead of simply executing a sequence of requests initiated by the user in a local dialogue with a narrow context for only a limited period of time, the AI agent must perform contextually appropriate tasks in a wider temporal frame, taking responsibility for the initiation and control of the interaction and for maintaining continuity of context across multiple separate exchanges with the user. Such new responsibilities must be managed with a higher degree of safety, reliability, and explanatory capability than the most current sophisticated AI models provide. This capability shift scenario would demand a more integrated design, placing the AI agent in a position to respond to the whole range of user prompts in a manner that is productive and efficient. Potential risks include complacency in the user-provided instructions and the temptation for human assistants to rely more heavily on the agent rather than doing the work themselves. Leading indicators of progress along this path include strong growth in the number of businesses deploying AI agents for these functions and emerging publicly available data on how well these systems perform comparative to traditional assistants.
Implications for Stakeholders: Businesses, Users, and Policy
Replacement of virtual assistants by AI agents would deliver unprecedented productivity gains. However, their emergence must be navigated carefully to avoid exhausting the limits of data and compute resources, and to mitigate negative impacts on user privacy, security, and the labour market.
A more conservative scenario, where AI agents only partially replace virtual assistants, would lead to a slower but steadier path to productivity gains. Despite not being regarded as a direct replacement, AI agents would still change business processes by automating tasks in ways that virtual assistants do not. If a hybrid model prevails, organisations may need to change policies and procedures, reorganise, and modify their workforce.
If businesses adopt the augmentation scenario, they will seek data privacy and security guarantees, along with stable, government-provided regulatory frameworks. Users will want to be reassured that interception of private conversations will not be exploited for commercial purposes. These factors would permit normal gathering of the data needed for responsible AI development, while enabling continued economic growth, business expansion, job creation, improved experiences, and increased access to AI technology. Timely regulation to address these privacy and security aspects would mitigate ethical frictions and enable the full potential of AI agents to be harnessed.
Regulatory developments at the crossroad of new AI and cloud technologies must also address how users will deal with privacy and security concerns. Would users prefer multiple AIs, one per cloud service provider, or a single personal AI agent? A loss of privacy would have severe consequences for AI training and users’ overall experience. AI could become a super-agent that can interpret any user’s request. Users could specify which cloud service provider they want to rely on, but these requests may be misused. Nevertheless, the current AI agent development wave may lead to bifurcation: users may abandon free cloud access in exchange for superior experiences without the collection of personal data.
Methodological Considerations for Forecasting
Various data sources inform the assessment. Performance benchmarks for current and future expected capabilities are taken from academic studies, vendor reports, and expert forecasts from the broader AI research community. Scenario analysis and expert elicitation apply the task categories defined in Section 3 to assess the set of capabilities needed to convincingly execute the categories of all tasks assigned to a single user. Time-series analysis identifies the changes implied by the productivity gains and constraints suggested by the analysis of the macroeconomic environment. Sensitivity analysis highlights emerging risks and identifies when and where the balance may be shifting.
Despite the consolidation of AI into various popular services, concerns remain surrounding digital infrastructure barriers to access. The widely reported reliability, safety, explainability, and other emerging or yet-to-emerge challenges are confirming series of required operational frameworks providing AI-use offices within organizations to minimize scandals. These offices should also seek to prove concepts with future developing organizations, seek future authorization of new uses external of company solutions, and collaborator solutions, thus imposing a negotiated form of digital presence without the need for a permanent or expanded legal digital identity for the operation of the infrastructure and AI agents Operating authorization, with simultaneous AML, privacy-opt, and other required regulatory compliance of Toy Chat GPT operations and other operational barriers.
Discussion: Evidence, Uncertainties, and Ethical Considerations
Forecasting the replacement of virtual assistants by AI agents in 2026 involves uncertainty in three areas. First, benchmarks assessing agent capabilities are necessarily limited in scope and content. Second, few of these indicators suggest real-world application is imminent in the near term. Third, although driving and limiting factors have been identified, the net balance of these opposing influences on progress remains hard to gauge. Addressing such concerns requires distilling the available evidence and questioning its reliability: Are recent predictions by AI developers credible? What remains a challenge for AI agents? Is it difficult to determine whether society is ready for AI agents or whether impact will be positive?
Detecting bias in AI models is an emerging area of study, encompassing fairness, accountability, and transparency. Although these areas do not present direct obstacles to the technological change under consideration, evidence suggests care must be taken to ensure that AI agents build user trust rather than undermining it by operating in obscurity. Developments in these three domains risk impacting users of all products that rely on LLMs and are therefore relevant to the analysis. However, no clear indicators are available that can help determine if AI agents will replace virtual assistants by 2026 or by any other date.
Several well-established technologies are being repurposed as AI agents, yet almost every real-world application is limited to a single task, either performed directly or indirectly via software-as-a-service API calls. An AI agent capable of replacing a virtual assistant in 2026 would need to execute a wide range of general tasks as well as human-level task management capabilities, while at least matching the other actors in modern labour markets by being able to learn new tasks from human feedback, be safe in its use, and explain how and why it produced the outcomes it did. Several accelerators—most notably large datasets, distributed training, high performance computing resources, and increased generalisation capabilities—are making it more likely that AI agents will be able to replace VAs in 2026 than 2031, but the introduction of such agents remains constrained by a lack of reliable, safe systems; an inability to retain context across multi-turn dialogues; a shortfall of businesses or other organisations with large numbers of business-wide SaaS tools deployed, supporting a single interface across the mixture of SaaS APIs from multiple companies; and the high cost of switching between using human labour markets and AI agent labour markets.
Consideration of three contrasting possible approaches for the development and deployment of AI agents leads to a similar conclusion. A higher probability of an AI agent replacing a VA in 2026 compared to 2031 hinges on an increase in the number and depth of real-world deployments that highlight the real-world weaknesses of delivering highly flexible human-level dialogue systems at scale and across domains. Despite this increased probability, however, it remains only a modest 30%–50%. Monitoring specific indicators should indicate the direction in which timelines for replacement are shifting. These indicators should include the number of companies developing and deploying AI agents; growth in human-like multi-turn conversations; the number of AVA tasks integrated into AI agents or covered by other products directly competing against AVAs; significant profitable deployments of AI agents at major compute or product-as-a-service suppliers; and the overall number of AI agents deployed on the web.
Thilina is a multi-skilled digital marketing professional and technical specialist at Sotavento Medios. He manages essential technical SEO audits, search engine indexing, and automated workflows. With experience spanning website management, Google Ads, and campaign execution, he ensures digital assets remain optimized for generative search engines.
