In 2026, agentic AI becomes an operational reality, no longer an experiment. Jensen Huang, CEO of NVIDIA, even believes that integrating AI with automation and robots might open up a $50 trillion industry in the real economy. That’s why businesses no longer ask whether autonomous systems work. What they are inquiring about is how to redesign data and control engineering to ensure that these systems can operate safely, efficiently, and at scale.

Conventional data and control engineering were designed to support human-driven processes. Data was gathered, cleaned, stored, and analyzed. Decisions followed later. Rules were implemented in control systems. This sequence is interrupted by agentic AI. Information, decision, and action now occur in a looping action.

From passive data pipelines to active data systems

Passive to active data systems. Traditional data engineering is based on ETL pipelines and warehouse centers. These systems are effective for reporting and analyzing history. They are not very well-suited to autonomous agents that require context.

The data platforms are becoming agent-ready in 2026. Information is searchable, semantic, and indexed. Knowledge graphs, vector stores, or enterprise search layers substitute rigid pipelines. Tables are not inquired about, but the meaning.

This change reduces latency. It also eliminates the need for manual data preparation. The agents can reason over both structured and unstructured data. The same decision surface is added to logs, documents, metrics, and transactions.

Data has ceased to be an input. It turns into a living object of action.

Control engineering moves from rules to intent

Control engineering has always been concerned with stability, predictability, and predefined logic. Deterministic decision trees, control loops, and think PLCs. Agentic AI proposes probabilistic reasoning and goal-directed behavior.

By 2026, control systems will be used to fulfill the intent, not to enforce rules. Human beings set goals, limitations, and precautions. Agents choose to behave within such limits.

This can be seen in information technology activities, production, and data platforms. The agents observe signals, diagnose states, and select corrective measures. The control logic is changeable based on the results.

What is obtained is adaptive control. Systems will react to change even without human intervention.

Multi-agent orchestration becomes the new control plane

Individual intelligent systems are not scalable. Enterprises in the contemporary world operate on inter-domain processes, based on composites. There is a relationship between data engineering, infrastructure, security, and applications.

This is solved with agentic AI by orchestration. Special agents do narrow tasks. Orchestrators arrange them. The present-day control engineering involves agent-agent communication, task assignment, and conflict resolution.

It resembles a microservices architecture. The agents are small, testable, and replaceable. The combination of them constitutes a self-sufficient workflow.

Control engineering teams now design the interaction protocols, escalation paths, and autonomy levels. It is made as significant as the choice of algorithm.

Governance is embedded, not added later

Innovations in control are needed for autonomous systems. Accessibility. Traditional governance requires human approval at each step. The same does not scale when agents are running continuously.

The system was designed in 2026. Every agent action is logged. Decisions are traceable. There is dynamic identity and authorization enforcement.

Zero-trust applies to agents in the same way it applies to human beings. Agents are authenticating each action. Accesses are temporary and limited. This limits risk but does not slow down the execution.

Observability, auditability, and explainability have become first-class engineering requirements in control engineering.

Data becomes digital exhaust for learning systems

All agent actions generate data. These outputs used to be thrown out as logs. In the agentic systems, learning signals are used.

This online stream drives performance optimization, anomaly detection, and post-training. Teams of data engineers are now designing feedback loops and pipelines.

Control systems become better with time. They get to know what works in what circumstances. This generates compounding gains of efficiency.

The challenge is scale. Human managers can’t screen millions of agent decisions. Other agents are monitored, evaluated, and optimized by new supervisory agents.

Human roles shift toward design and oversight

The agentic AI does not eliminate human beings in the data and control engineering. It changes their role.
Engineers concern themselves with the architecture, constraints, and behavior of a system. They are specifying goals, safety limits, and escalation rules. Instead of being routine, they act at exception points.

This causes increased leverage in work. A single engineer can now monitor systems that would previously have required large teams.

The border between data engineering and control engineering is also unclear. The two fields have become centred on facilitating independent decision loops.

Conclusion

When enterprises redesign around agentic AI, they will make decisions more quickly, reduce operational costs, and may be more resilient. Those that just automate the old processes reach limits. The distinction is in essentials. Ready-to-use data architecture of the agent. Goal-oriented control systems. Orchestrated agents. Embedded governance.

By 2026, agentic AI is no longer an option. It is an operating model. It has data and control engineering at its heart. Organizations that master this change with Chapter247 are not automating work only. They are reinventing the ways of thinking, making decisions, and taking action within systems.