Agentic artificial intelligence is redefining the foundations of enterprise infrastructure. At VB Transform 2026, Meta's VP of Engineering Barak Yagour issued a warning that resonates across the sector: organizations have approximately 20 months to rebuild their systems, originally designed for human users, and adapt them to an ecosystem where AI agents will be the primary consumers of data and services. This transformation is not theoretical; at Meta, agent-generated queries grew 30-fold in just six months, and automated traffic has already surpassed human traffic on the internet, according to reports from Imperva and HUMAN Security. For companies aiming to stay competitive, the time to act is now.
Yagour identified three pillars breaking simultaneously: capacity, identity, and velocity. Regarding capacity, the traditional relationship of one engineer generating one unit of load is no longer valid. A single developer can deploy dozens of agents, each with its subagents, multiplying system demand by a factor of one hundred. The solution is not to block this traffic but to make infrastructure agent-aware, implementing dynamic controls that understand hierarchies, attribute costs, and prioritize requests based on importance. Here, the development of custom software plays a fundamental role: every organization needs personalized solutions that can distinguish between a high-level agent and a simple automated query.
The second pillar, identity, collapses because agents do not fit traditional access control models. They are not human, they do not have physical credentials, but they make autonomous decisions. Companies must rethink their authentication and authorization systems, integrating mechanisms that verify each agent's origin and purpose. Cybersecurity then becomes a critical enabler; it is not just about protecting data but ensuring every agent interaction is traceable and governed. Solutions like those offered in artificial intelligence from Q2BSTudio can help companies design environments where agents operate with controlled autonomy.
Velocity is the third challenge. Tools like GitHub Copilot already write nearly half of the code, but the rest of the pipeline —compilation, testing, deployment, monitoring— does not magically accelerate. Continuous integration and deployment (CI/CD) infrastructure must evolve to handle machine-generated code volume while maintaining quality and security. Here, process automation combined with a cloud AWS or Azure approach enables testing and deployment environments to scale without bottlenecks. Q2BSTudio, as a software and technology development company, helps its clients build adaptive pipelines that absorb this new pace.
At the heart of this transformation lies data management. Meta is implementing what it calls 'trusted data environments,' where agents can freely explore information, but every output is traced back to its origin and scrutinized. Sensitive fields are masked before an agent can access them, and each request is evaluated in real time. This broad exploration, narrow release approach is key to maintaining control without stifling innovation. Companies working with BI and Power BI consulting, for example, are redefining their dashboards to be consumed directly by agents, not just human analysts. Analytics must be contextual and real-time, an area where Q2BSTudio offers Business Intelligence solutions adapted to new demands.
Reasoning models are changing the data layer. It is no longer enough to rely on correlation patterns over summarized signals; systems need the full behavioral history to understand user intent. This requires replacing batch ETL processes with real-time streaming, and storing data so that the system knows its schema to avoid overfetching. Meta is preparing to handle 500 million queries per second and a petabyte per second of throughput for training data reads. Such scale is only possible with a robust cloud architecture, whether on AWS or Azure, that supports elasticity and low latency.
The final impact is seen in recommendation systems. Yagour noted that 42% of Instagram users want to fundamentally change the algorithm, not just tweak a single session. Meta's response is fully conversational recommendations, where the system reasons about intent rather than matching keywords. A term like 'soccer' returns different results for a casual fan versus an elite athlete. This deep personalization requires agentic infrastructure that understands context and purpose.
Yagour described these three threads —agents, data, and recommendations— as a self-reinforcing flywheel. Agents make data more accessible; better data enables reasoning; reasoning creates new demands that push agents and infrastructure forward. For companies, the 20-month window is real. Those who do not act now risk being left behind in a world where humans and agents co-create at scale. Partnering with a technology provider like Q2BSTudio, specialized in custom software, cloud, and cybersecurity, can make the difference between leading the transformation or being overtaken by it.
