Blog The Truth About AI Agent Risks And What To Do About Them

AI agents are no longer emerging tools — they are becoming operational teammates across software development, workflow automation, customer service, HR, cybersecurity, and analytics. These agents are not just supporting teams but reshaping how work gets done at scale.

The technology that makes it possible is a hybrid AI architecture called Retrieval-Augmented Generation (RAG). RAG can enhance a Large Language Model’s (LLM) output by injecting relevant knowledge from trusted external sources, instead of relying solely on pretraining. RAG uses these trusted sources to generate more informed, grounded responses. It’s context-aware, fast, up-to-date, and based on your own data.

AI agents are designed to assist users in completing complex or time-consuming jobs, completely autonomously. Unlike traditional automation tools that follow predefined processes, AI agents use Machine Learning (ML), Natural Language Processing (NLP), and LLMs to adapt, learn from interactions, and make decisions. They can carry out multi-step, nuanced tasks with a high degree of autonomy.

2025 marks a major turning point for AI agents as real-world adoption meets enterprise urgency. The emergence of powerful, generalized language models like GPT-4, Claude, and Gemini has provided a flexible, natural-language backbone that enables agents to reason, generate, and interact across tasks. Cloud and edge computing costs have decreased while performance has increased, enabling organizations to run AI agents with real-time responsiveness and without prohibitive infrastructure investments. RAG allows AI agents to work with up-to-date, trusted business knowledge, reducing hallucination and unlocking enterprise-grade applications.

Platforms like AutoGPT and others have lowered the barrier to entry for building and orchestrating agentic workflows. These frameworks abstract complex logic and make it easier to prototype, deploy, and scale agents. As AI governance practices mature, companies are becoming more confident in their ability to adopt AI responsibly. Tools offer auditability, role-based access control, and compliance alignment, helping bring AI out of the lab and into production.

The most prominent and practical categories of AI agents in use today are:

Voice agents

These AI agents use NLP and speech recognition in conversational interfaces via voice (phone bots, call center assistants, smart speaker apps). Voice agents are a good fit for customer service, internal help desk, and for accessibility options.

Agentic RAG

Agentic RAG powers many knowledge and workflow agents. Great for knowledge retrieval, along with creation and development platforms. Combines precise knowledge retrieval with generative AI. Supports internal FAQs, Q&A, documentation agents, summarizers, and real-time advisory roles.

UI interaction agents

These agents can navigate the User Interface (UI) of browsers and search the web like humans do. Also called “browser agents” or “Auto-GUI agents” they’re used to navigate web apps or desktop UIs the same way a human does (clicking, scrolling, filling out forms). Useful for legacy applications, low-API environments, and automation of repetitive workflows.

Coding agents

Used for building and debugging applications faster. Specialized for writing, debugging, and testing code. They’re often paired with IDEs, CI/CD platforms, or internal development pipelines.

Tool-based agents

Niche AI agents created to use specific tools based on user workflow. Can handle tasks like emails, web searches, filing tickets, CRM entries, and automated Slack responses. Is often integrated into specific software ecosystems such as Workfront, Salesforce, Outlook, Notion, or Slack.

Multi-modal orchestrators

These are meta-agents that combine the above modalities. Imagine an AI agent that reads a document (RAG), fills a form (UI), and sends a Slack update (tool-based) to keep team-members informed.

Some experts group tool-based and UI interaction agents under the term “action agents” because they’re focused on doing, while RAG and voice agents fall under “information agents” because they are focused on knowing and telling what they know.

Due to their flexibility, range, and ability to scale easily, AI agents are poised to become a core part of modern business infrastructure.

Of course, the first step to any successful AI adoption is a solid data infrastructure.

The AI agent market takes flight: Billions in growth ahead

The AI agents market is projected to grow from $7.84 billion in 2025 to $52.6 billion by 2030, reflecting a Compound Annual Growth Rate (CAGR) of 46.3%. Deloitte forecasts that in 2025, 25% of companies using generative AI will start agentic AI pilots or proofs of concept, rising to 50% by 2027. McKinsey estimates AI could add an additional $2.6 trillion to global productivity.

Implementing AI agents offers increased efficiency by automating routine tasks, resulting in customer service productivity gains of 30 to 45%, according to McKinsey. They also lead to operational cost savings by reducing manual labor and errors, providing data-driven insights for enhanced decision-making, and delivering tailored experiences for personalization to improve customer satisfaction.

Imagine a global logistics company managing hundreds of shipments, vendors, and customer requests each day.

Before adopting AI agents, operations teams were buried in manual tasks — inputting shipment details, triaging customer inquiries, generating reports, and cross-referencing supplier data. Delays and data entry errors were common, and the support team was stretched thin handling routine questions.

After implementing a suite of AI agents, positive outcomes piled up.

A workflow automation agent began syncing order data across ERP and CRM systems, reducing clerical work. A voice and chat agent took over first-level customer support, handling most inquiries — from tracking packages to resolving billing questions — with natural, contextual responses. Meanwhile, a RAG-powered internal knowledge agent helped warehouse managers make smarter, faster decisions by providing real-time insights on supplier delays and routing efficiency.

With these agents working in parallel, employees can shift focus from task execution to strategic problem-solving. The company can increase overall productivity, drop expensive operational costs, all while customers experience faster, more personalized service.

The smart way to scale AI agents and avoid technical debt traps

As AI agents accelerate coding, automate workflows, and enhance decision-making, they also introduce a new class of technical risks — particularly when deployed without proper oversight or architectural alignment.

For example, over-reliance on coding agents can lead to bloated or unmaintainable codebases if outputs aren’t reviewed or structured properly. Agents that shortcut quality assurance, bypass version control standards, or implement logic without context may inadvertently generate fragile systems. Likewise, workflow automation agents that operate without documentation or change tracking can obscure process logic, creating long-term visibility and maintenance challenges.

The root issue isn’t technology, it’s how it’s implemented. As with any new layer of abstraction, AI-driven automation must be governed to remain sustainable.

To balance short-term gains with long-term stability, organizations should take a deliberate approach:

• Keep humans in the loop: Validate agent-generated code and outputs, especially in regulated or high-risk environments.
• Document agent actions and workflows: Ensure that teams can audit, trace, and hand off AI-assisted work.
• Align agent behavior with architecture standards: AI outputs should fit within your existing infrastructure and dev practices — not define new ones in isolation.

AI agents can absolutely speed delivery and improve consistency. But without careful oversight, they can just as easily accelerate entropy. Avoiding hidden debt means treating agents as collaborators — not black boxes — and maintaining the same rigor you’d apply to any high-impact system change.

Shadow AI and AI agents: Innovation without exposure

The rapid rise of AI agents introduces real risks when adoption happens outside the purview of IT or governance teams. This phenomenon, known as shadow AI, is already taking root in many enterprises.

Employees eager to improve productivity are experimenting with generative AI tools, integrating coding agents into dev workflows, or deploying automation scripts — all without formal oversight. While this speaks to the demand for AI-driven innovation, it also creates potential vulnerabilities.

Unmanaged agents, particularly those acting autonomously, can inadvertently access sensitive systems, introduce data privacy risks, or create compliance blind spots. Over time, these ad hoc deployments erode governance structures and make it harder to scale responsibly.

Rather than shutting down experimentation, organizations should aim to channel it productively. That means establishing clear AI usage policies, creating approved agent platforms, and offering sandboxes for safe prototyping. Encouraging innovation doesn’t require giving up control, it just requires the right boundaries.

By enabling teams to explore agentic capabilities within secure, transparent frameworks, enterprises can avoid shadow AI risks while still fostering the creativity and efficiency that make AI agents so valuable in the first place.

See it in practice. Discover 6 high-impact agentic AI use cases for your organization.