Major Agentic AI Concerns For The Enterprise Systems Group

Introduction

The enterprise technology landscape is currently undergoing a seismic shift from generative AI, which creates content, to agentic AI, which executes actions. Unlike their passive predecessors, autonomous agents possess the capability to plan, reason, and interact with enterprise systems to complete complex workflows without direct human intervention. While this transition promises unprecedented operational efficiency, it simultaneously introduces a new class of systemic risks that the Enterprise Systems Group must address. The move to agency transforms AI from a tool that offers advice into an entity that holds the keys to critical infrastructure. This report outlines the four primary domains of concern – security, infrastructure stability, observability, and financial volatility – that must define our architectural and governance strategies moving forward.

The Security Crisis of Non-Human Identities

The most immediate threat introduced by agentic AI is the proliferation of high-privilege, non-human identities. Traditional Identity and Access Management (IAM) frameworks are designed for human users with relatively static behaviors and predictable session times. Agents, however, require persistent access to multiple systems – CRMs, ERPs, and databases – often chaining credentials across these environments to complete a single task.

The most immediate threat introduced by agentic AI is the proliferation of high-privilege, non-human identities

This creates a phenomenon known as “credential sprawl,” where thousands of autonomous agents possess active API keys and authentication tokens. If a single agent is compromised through prompt injection or adversarial manipulation, it effectively becomes a trusted insider with the ability to exfiltrate data or corrupt records across the entire enterprise stack. The risk is not merely unauthorized access but “agent hijacking,” where an attacker redirects an agent’s approved workflow to malicious ends, bypassing standard perimeter defenses because the traffic originates from a legitimate internal service.

Infrastructure Fragility

Enterprise infrastructure is rarely designed for the speed and volume of autonomous interaction. Most legacy systems – including core banking ledgers, supply chain trackers, and HR databases – were built with the assumption of human-speed operations. A human operator might query a database ten times an hour; an agentic workflow might query it ten thousand times in a minute while attempting to resolve a complex dependency. This mismatch creates a significant risk of inadvertent denial-of-service attacks launched by our own internal tools. Furthermore, the “brittle integration” problem becomes acute when agents attempt to navigate systems with inconsistent schemas or unstructured data. Unlike humans, who can intuitively bridge the gap between a spreadsheet and a database field, an agent encountering “dirty data” may enter a recursive error loop, continuously retrying a failed action and flooding the network with redundant requests. The stability of core enterprise systems relies on valid inputs, and an unmonitored agent has the potential to corrupt data integrity at a scale impossible for human users to replicate.

The Black Box Problem

Governance is severely compromised by the opacity of agentic decision-making. In traditional software automation, workflows are deterministic; if X happens, the code executes Y. Agentic systems, however, are probabilistic. They “decide” how to solve a problem based on context, meaning they may take different paths to achieve the same outcome on different days. This non-determinism makes standard auditing and debugging extraordinarily difficult. When an erroneous financial transfer occurs or a wrong vendor is emailed, the Enterprise Systems Group must be able to trace the “chain of thought” that led the agent to that specific action. Current observability tools track system performance (latency, uptime) but often fail to capture the semantic logic of AI decisions.

Without a dedicated “AI Trust Layer” that logs prompts, reasoning steps, and tool invocations in real-time, the enterprise faces a “black box” scenario where it is responsible for actions it cannot explain or reconstruct for regulators.

Operational Runaway

The final major concern focuses on the direct financial implications of unchecked autonomy.

Agentic AI models operate on a token-consumption basis, often utilizing expensive, reasoning-heavy large language models (LLMs) to plan their next steps. A poorly prompted agent or one stuck in a logical loop can consume massive amounts of compute resources in a short period. This “runaway cost” scenario is unique to agentic workloads, where a simple request can spiral into an infinite sequence of API calls and model inferences. Beyond compute costs, the operational liability extends to the agent’s external actions. An autonomous procurement agent that hallucinates a discount or misinterprets a contract term could legally bind the enterprise to unfavorable agreements. The financial risk is therefore twofold: the direct cost of the compute resources and the potential liability incurred by the agent’s unsupervised decisions in the market.

Conclusion

Addressing these concerns requires a fundamental rethinking of our systems architecture. We must move beyond standard API integrations to a “Zero Trust for Agents” model, where every agentic action is verified in real-time against strict policy constraints, regardless of the agent’s internal privileges. Infrastructure must be fortified with rate-limiting and “circuit breakers” specifically designed to cut off autonomous agents that exhibit recursive or aggressive behavior. Finally, we must mandate “human-in-the-loop” checkpoints for all high-stakes transactions until our observability frameworks mature. The Enterprise Systems Group must treat agentic AI not just as software to be deployed, but as a new workforce to be managed, secured, and audited with the same rigor applied to human employees

References: