As enterprises embrace Agentic AI, they’re discovering that autonomy scales faster than trust. However, enterprises have successfully implemented autonomous systems long before LLMs, especially during the mainframe era, where end-to-end workflows ran with minimal human intervention — from transaction initiation to data persistence.

Mainframes were designed to delegate tasks with accountability, and their architecture shares similarities with modern Agentic AI systems. Mainframe systems used deterministic business rules (e.g., COBOL) and centralized transaction control (e.g., CICS), whereas Agentic AI systems leverage microservices, agents, and cloud-based orchestration. Data was managed through durable system memory and relational databases, contrasting with Agentic AI’s use of cloud data warehouses and vectors.

Both systems rely on autonomous workflows, scheduled agents, event-driven agents, state consistency, and human-in-the-loop mechanisms. The main difference lies in the intelligence layer, where mainframes were rule-based and deterministic, while Agentic AI systems are probabilistic and adaptive.

Mainframes were also built with robust governance, ensuring accountability through mechanisms like change control, audit trails, and deterministic replay. These systems prioritized correctness, reliability, and scale, while modern AI systems often struggle with governance, failing to implement policies before deployment.

From the 1990s onward, changes in software architecture and developer velocity, such as object orientation and stateful services, led to rapid iteration but also a loss of execution ownership and predictable failure modes. This shift towards speed and abstraction replaced predictability, creating new challenges that Agentic AI now addresses by revisiting core mainframe principles like determinism, stateless execution, and idempotent processes.

Mainframes taught us that autonomy requires boundaries, and governance must be built into the architecture, not added later. Trust is earned through reliability, not sophistication. Failure modes must be explicit, and human oversight should be part of the design. The success of Agentic AI will depend not just on model sophistication but on whether organizations engineer trust through clear boundaries, predictable execution, and fail-safe mechanisms.

The author is Sai Krishnan Mohan, Vice President (Data & Analytics), Bajaj Auto Ltd.

Disclaimer: The views expressed are solely of the author and ETCIO does not necessarily subscribe to it. ETCIO shall not be responsible for any damage caused to any person/organization directly or indirectly.

Published On Feb 21, 2026 at 09:00 AM IST

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