The Agentic Era Is Here: It Is Not About Chatbots Anymore
- May 11
- 8 min read
Updated: 2 days ago
At Google Cloud NEXT 2026 in Las Vegas this April, with tens of thousands of attendees across in-person and global sessions, one shift became impossible to ignore.
The conversation has moved beyond chatbots.
For the past three years, enterprise AI was a procurement question: which model, which API, which vendor. Teams built demos, ran pilots, and shipped assistants that answered questions. That era produced genuine value, but it also left a long tail of proofs of concept that never made it to production.
A new operating model is emerging. AI systems that do not just generate responses, but reason, plan, and execute across workflows.
This is what the industry is calling agentic AI.
Agentic AI refers to systems that can independently plan and execute multi-step tasks using tools, data, and defined constraints. These systems do not just respond to prompts. A model that drafts an email is a feature. An agent that monitors a data pipeline, detects an anomaly, drafts a remediation plan, routes it for human approval, and executes confirmation is a system.
But the real shift isn't the term. It's about how systems are designed, governed, and operated.
The Shift Is Not About Models Anymore
For the past two years, enterprise AI has focused on models:
Which LLM to use?
How to fine-tune it?
How to improve prompts?
That layer still matters. But it's no longer a bottleneck. The constraint has shifted from the model to the surrounding system. Think about what agentic architecture introduces:
Multi-step reasoning. The agent doesn't just respond. It plans, executes, evaluates, and revises. A customer service agent who resolves a billing dispute isn't completing a single task; they're running a workflow that touches CRM data, policy rules, communication templates, and escalation logic.
Tool usage and API interaction. Modern agents do not just generate text; they call functions, query databases, and trigger automations. This is not a prompt engineering problem. It is a system integration problem.
Long-running, asynchronous execution. A model answers in seconds. An agent might run for minutes or hours, or span sessions, coordinating with other agents, waiting for human approvals, and processing background jobs. This requires state management, error handling, and retry logic that is independent of the model itself.
Decisions with real consequences. When an AI agent books a meeting, moves budget, triggers a supplier order, or flags a compliance issue, that is not a suggestion. It is an action. The bar for the surrounding system is fundamentally different from a chatbot giving a slightly off answer.
This moves AI from a feature to an operational layer inside the enterprise.
That changes everything.
What Actually Changed at Google NEXT
Google's announcements at NEXT 2026 were not just feature updates. They reflect a deliberate architectural shift: from models as products to models as components inside larger systems.
Gemini is becoming an agent platform, and so is the broader Vertex AI stack. Vertex AI - the platform that has long served as the umbrella for Google's AI products- is now repositioning itself as an Agent Platform. Gemini Enterprise sits within this, gaining capabilities for long-running autonomous agents, grounding via a Knowledge Catalogue, and centralised oversight through an Agent Inbox. One design choice still stands out: agents escalate to humans. They do not replace them. That is not a limitation. It is a governance decision baked into architecture.
Building agents now look like engineering. The Enhanced Agent Designer introduces natural language and visual workflows, hybrid logic combining generative and deterministic steps, and built-in human checkpoints. This is where AI development stops looking like experimentation and starts looking like system engineering. If your team is still treating agent-building as a data science exercise, the tooling is ahead of the process.
Reusable skills unlock scale. Reusable Skills allow organisations to encode policies, formats, and compliance logic once, and have agents automatically apply them. Instead of re-prompting, the system just knows. This is where AI starts behaving like enterprise software: consistent, auditable, and maintainable. It is the difference between an AI that performs and one that can be relied upon.
Data agents address the real bottleneck. Google's Data Agent Kit, covering Data Engineering Agents, Data Science Agents, and Observability Agents, addresses a problem most teams already feel acutely. There is no data shortage. It is an execution gap. The data exists. The pipelines do not reliably move them. Agents cannot be grounded in data they cannot access.
Security becomes active, not reactive. With Wiz integration and new security agents, threat hunting becomes continuous, detection engineering becomes automated, and external signals are operationalised in near real time. Security is shifting from monitoring to continuous adaptation. The implication is that security is no longer a gate at the end of a deployment cycle. It's a running process.
Infrastructure is being rebuilt for always-on AI. New TPUs and deeper GPU integration signal a clear separation between training and inference workloads, with major improvements in cost-performance ratios. Because agentic systems don't run occasionally, they run continuously. The economics of inference at scale are now a design constraint, not an afterthought.
Where This Breaks in the Real World
The announcements are compelling. The tooling is maturing fast. But the limiting factor isn't access to technology.
It's readiness.
Agentic systems don't fail because of the model. They fail because the surrounding system isn't ready. Most organisations are already experimenting with agents. Very few are actually ready to run them in production. In practice, four challenges consistently arise:
Fragmented data foundations. Data is spread across systems, inconsistently governed, and hard to access in real time. Agent grounding depends entirely on this layer. An agent that can't trust its inputs can't make decisions worth trusting.
Missing execution infrastructure. Many environments still lack standardised orchestration, strong API layers, and reliable pipeline automation. Agents need a system they can act within, not just think within. You can build the most capable reasoning layer in the world; if it can't call a reliable API, it stalls.
Governance wasn't built for autonomy. Most governance frameworks were designed for human workflows: approval chains, audit trails, and accountability structures that assume a person is making the call. Agentic AI requires governance built into the system, not layered on top. Common gaps include policies that don't translate into automated decisions, limited auditability of AI actions, and a lack of structured human-in-the-loop mechanisms.
No clear ownership of the system. AI is still treated in many organisations, either as a data science initiative or as a product feature. It's neither. It's a cross-layer system spanning data, infrastructure, security, and applications. Without ownership at that level, someone accountable for the whole stack, progress stalls at the boundaries between teams.
A Simpler Way to Think About Readiness
One useful lens is the Agentic Readiness Stack, which is five layers, each of which must be stable before the one above it reliably works:
Data foundation - unified, governed, accessible
Execution layer - pipelines, APIs, orchestration
Control layer - governance, observability, human checkpoints
Agent layer - reasoning, planning, tool usage
Business integration - real workflows and decisions
Most organisations are experimenting at the top while still stabilising the bottom. That gap is where initiatives break. A team can have a functioning agent prototype on Friday and discover Monday that the data it depends on isn't reliably available; the API it calls doesn't have a stable contract, and no one owns the decision when it gets something wrong.
What This Means in Practice
Three implications stand out.
Data work becomes a critical path. Agentic AI immediately exposes weak data foundations. Fragmented pipelines, inconsistent schemas, and unreliable access. These were manageable when a model was generating a summary. They become blockers when an agent bases its decisions on that data. This is no longer a technical debt. It's a capability blocker.
Governance becomes an architecture. Human-in-the-loop, simulation environments, auditability, and rollback logic. These are now system design decisions, not compliance checkboxes. The organisations that will deploy agentic AI responsibly are the ones that are designed for governance from the start, not the ones that tried to add it after the fact.
The PoC phase is ending. The constraint is no longer experimentation. There are more than enough tools, frameworks, and capable models to build interesting things. The constraint is execution, getting from a working prototype to a production system that can be monitored, updated, and trusted at scale.
The Foundation Determines the Ceiling
Google Cloud NEXT 2026 wasn't just a product event. It was a signal of how enterprise AI systems are expected to operate going forward, and a clear marker of how quickly the bar is rising.
But the real gap isn't between organisations and technology. Technology is largely there. The gap is between AI ambition and system readiness.
At TeraSky Europe, we work with Platform Leaders and Data & AI Leaders across the Baltics and broader EU who are navigating exactly this transition. What we see consistently is that the organisations making real progress aren't the ones with the most advanced model access; they're the ones that invested early in the foundations: governed data, reliable pipelines, and an architecture that can support autonomous execution without breaking.
That's where we focus. Not on selling a stack, but on diagnosing where the system will break under real conditions, and building what's missing, end-to-end, from data foundation to production-grade agent deployment.
The conversations we're having now sound different from a year ago. Leaders aren't asking 'Should we do AI?' anymore. They're asking, 'Why isn't ours working yet?' More often than not, the answer isn't the model. It's everything underneath it. Our job is to close that gap. From the data layer up to the point where AI is actually executing decisions in production, not just sitting in a proof-of-concept." Greta Mieliauskaitė , Account Executive, TeraSky Europe
In simple terms, AI is moving from answering questions to executing work, but only organisations with the right systems will benefit. Most of the real work isn't building agents. It's fixing what they depend on: data pipelines that don't scale, identity models that don't support delegation, and governance frameworks that were never designed for autonomous execution.
The organisations that will move fastest won't be the ones talking about agents. They'll be the ones quietly rebuilding their foundations with the right partner.
Infrastructure is being rebuilt for always-on AI wherever you run it.
A shift that deserves attention: Google announced that agentic platforms are now being designed to work on-premises as well, for organisations that are not ready or willing to move fully to the cloud. For those already in the cloud, the tooling works across infrastructures - AWS, Azure, or Google Cloud - removing the assumption that AI adoption requires a single-vendor commitment.
The less visible but increasingly consequential part of this is token economics. Many organisations experimenting with AI agents are discovering that compute costs can outpace the cost of the developers building them. Google's response is practical: usage limits are now available in preview, giving teams guardrails before costs escape controlled experiments. The implication is that almost any infrastructure can now access AI tools. The real question is at what efficiency and at what cost.
This is where the gap between experimentation and production becomes expensive. Choosing the wrong architecture, over-provisioning agent workloads, or running without usage governance can turn a promising pilot into a budget problem fast. Infrastructure and cost architecture are part of how we design AI systems from the start, not variables left to figure out after deployment. Whether you are running Google Cloud, a hybrid setup, or evaluating an on-premises path, we help organisations make the right infrastructure decisions before they become the wrong cost decisions.
