Agentic AI & the Growing Reputation Risks For Global Brands

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Recent AI usage numbers are astronomical — ChatGPT is reaching above 800 million weekly users and Google’s Gemini-powered AI Overviews exceeded 2 billion users per month. Adoption like this exceeds even the wildest of expectations. 

Agentic AI is the next genAI evolution and, while its advanced, autonomous features are a productivity win, the technology also brings new reputational risks to the fore. 

Growing AI investment, adoption, and complexity poses new challenges for global brands and leaders trying to maintain trust, authority, and value. When already-powerful AI can now operate independently and at scale, this risk picture changes.

Here’s a deep dive into agentic AI to help you get up to speed, plus a quick brand risk assessment now that AI agents are increasingly in play.

What is Agentic AI?

Agentic AI refers to AI systems that can achieve complex objectives by independently reasoning, planning, and acting with very limited human oversight. Agentic models can interpret intent, set goals, and perform multi-step actions across platforms and tools on behalf of the user, not just generate outputs.

ChatGPT’s Agent Mode is one example. Agent Mode functions just like ChatGPT, but has advanced problem-solving features like access to its own virtual computer and broader permissions. This means it can automate complex workflows like research and analysis.

In practical terms, agentic AI transforms AI from a reactive assistant into an autonomous operator, working for the user at scale. And it’s beginning to reshape how businesses run, how consumers make choices, and what brands must do to maintain control of their narratives.

Agentic AI in action

The typical workflow looks like this:

1. You provide the agent with a goal (e.g., research competitors to find opportunities for growth).
2. The agent launches a virtual desktop, performs web searches, opens sites/PDFs, and gathers data.
3. It evaluates information using credibility signals, choosing reliable sources.
4. It runs code or calculations when needed (e.g., market penetration).
5. It generates relevant content when needed.
6. It compiles everything into a finished report with rankings, analysis, and recommendations.

Agentic AI Characteristics

Here’s how agentic AI compares with standard genAI chatbots:

• Autonomous action — Handles multi-step tasks independently to accomplish objectives.
• Goal-driven reasoning — Interprets intent and determines the most effective path forward.
• Planning and sequencing — Breaks down complex goals into structured, executable steps.
• Cross-system integration — Coordinates actions across apps, APIs, and data systems.
• Context awareness — Adapts as new information, feedback, or conditions emerge.
  Continuous learning — Builds memory over time to refine performance and accuracy.
• Delegated decision-making — Acts responsibly within defined brand or user parameters.

The Risk Landscape: Agentic AI

Agentic AI creates two vulnerability categories for big brands: internal and external. 

Internal AI risks stem from brand deployment of agentic AI without sufficient governance, best practices, and ethical oversight. 

External AI risks are ambient vulnerabilities that agentic AI creates for your reputation when used by consumers. For our purposes, we’ll stick to the biggest external, third-party risks.

Brand misrepresentation 

Agentic AI’s brand narrative implications are far-reaching and a few different dynamics come into play. Just like non-agentic AI, AI agents only generate based on what’s out there. 

Outdated, biased, and incomplete data can fuel negative agentic findings and output. These errors can be repeated at scale, creating new facts about your brand, adding kindling to narrative damage. 

Any non-positive, inaccurate output threatens your narrative and as AI-driven technologies continue to reshape public perception of brands and executives, the risk grows exponentially.

Impersonation and false associations

It’s possible for malicious actors to deploy AI to harm brands through various types of impersonation, like mimicking brand voice, leadership personas, and offerings. But agentic AI can actually disperse and spread this content at scale and appear to be legitimate. 

There’s the potential that an audience won’t be able to tell if they’re engaging a brand or facing AI-powered fraud. The potential brand damage from an AI agent posing as a CEO, for instance, is virtually unlimited.

Agentic AI can also rapidly access controversy, compile it, and associate it with your brand. Even if it’s just a contextual error, the guilt by association is lasting. Over time your brand name becomes connected to negative terms. Think: “[brand name] lawsuit” as an example. This technology can repeatedly surface outright errors and things you left in the past. 

Misinformation loops

The days of AI only learning from what its developer gives it or legitimate material online are ending. Today, AI learns from AI, whether it’s from AI-generated content or agents directly accessing AI as part of a prompt. 

Proving the point, Ahrefs analyzed 900,000 new websites and detected “substantial AI use” across nearly half of the domains. Trends take on a similar trajectory elsewhere on the web, meaning that AI is essentially sourcing itself, errors and all. 

As agentic AI advances and gains greater adoption, a closed misinformation circuit that recycles, loops, and regurgitates brand-damaging material may arise. Correction after the fact may be nearly impossible.

Specific Agentic AI Use Cases and Risks

Agentic AI will be applied in new, diverse ways, but brands should be aware of how it could impact reputation and business. It’s enough to give brand executives nightmares.

Potential Uses	Related Risks
Social or content agents that curate news, social posts, and commentary for users.	Agent cherry-picks the worst stories about a brand or individual, creating, sharing, and amplifying a damaging narrative.
Personal AI advisors that summarize brand reputations or recommend products.	Agent continuously digs up past controversies, reintroducing them into the public discourse again and again.
AI assistants for journalists or investors that summarize brand performance or executive reputation.	Agents become a go-to tool for investigative journalists, data for hit pieces, and fodder for negative coverage on social media.
AI shopping agents that autonomously compare prices, reviews, and social sentiment before buying.	Agents give consumers instant and constant access to negative sentiment around brand offerings that damages trust.

AI’s risk multiplier

The indirect risks associated with agentic AI and AI in general are multiplied by the unique characteristics of the large language models (LLMs) themselves. These characteristics include:

• Untraceable origin — You can’t identify or control which AI system created the narrative.
• Persistent distribution — Once indexed by multiple models, misinformation reappears indefinitely.
• Ambiguous accountability — Regulators, media, and users still hold the brand responsible.
• Velocity mismatch — AI-generated distortions spread faster than brands (without help) can detect or counter.

Together, these elements add gravity to the situation, increasing the cost, lifespan, and urgency of brand reputation harm.

Beyond human oversight

Agentic AI and future evolutions continue to add complexity to managing one’s reputation. The autonomous power of AI agents creates a parallel reputational track formed not by human discussion and perception, but wholly by AI. 

Now AI plays a larger role in determining your reputation, building its own framework to tell your story.

How reputation management helps

So, what can you do to ensure your brand’s narrative stays intact as AI continues to transform the digital landscape? Online reputation management is one of the strongest answers.

Agentic AI needs sources to pull, whether from Google or other AI platforms. Brands must proactively create controlled, targeted, strategic content to offer what agentic AI is looking for. 

When users task AI with things like brand comparisons or even deep reputational digging, controlled sources get ingested, defining the overall narrative.

Additionally, reputation management should be stacked with reputational landscape monitoring to identify new threats, find opportunities, and steer your strategy in real time. 

Brands can’t just manage perception after the fact, they have to proactively engineer trust, establishing an AI-friendly reputation strategy built to excel even as the environment rapidly changes.