Artificial Intelligence (AI) Secret Agents

From Tool to Teammate: The Rise of AI Agents

Unlike passive Learning Language Models (LLMs) or scripted chatbots, AI agents act. Tools like Microsoft Copilot, AutoGPT, and Salesforce Einstein are beginning to interpret context, plan multi-step actions, and execute those actions without direct instruction. Examples include: contract review, customer complaint triage, or even insurance pricing model updates.

This evolution presents a new class of operational risk. We’re no longer supervising tools; we’re managing co-workers who write code, send emails, and generate strategy decks, sometimes with access to sensitive systems. There is a significant operational efficiency benefits to businesses. Consequently, there is also significant risk exposure.

When Agents Hallucinate

Generative AI systems are prone to “hallucinations”; confident, often dangerous, inaccuracies. In 2023, a New York attorney used ChatGPT to prepare a court filing, only to find that the citations were entirely fabricated. In another case, an AI-powered hiring tool trained on past data systematically downgraded female applicants for technical roles.

These aren’t mere glitches. They’re systemic errors baked into algorithmic reasoning. When AI agents act on hallucinated information, they create direct liability. Enterprises face exposure in:

• Errors & Omissions (E&O) when AI miscalculates or misadvises
• Cyber liability when data used by the AI is inaccurate, stolen, or leaked
• Employment Practices Liability (EPL) when biased decisions are automated

Who’s Accountable When the Bot Breaks Bad?

Legal frameworks are lagging. If an autonomous AI writes defamatory content, recommends a discriminatory hire, or initiates an unauthorized financial transaction, who is responsible? The user? The developer? The enterprise?

This liability gray zone is a growing concern for Directors & Officers (D&O) insurers. As boards authorize AI adoption without clear governance, they risk shareholder backlash, regulatory scrutiny, and reputational damage. The SEC has already indicated it is watching how companies disclose material AI risks. Oversight isn’t optional. It’s fiduciary.

Cyber Risk: Invisible Entry Points

AI agents can become unintentional backdoors. Their connectivity to Application Programming Interfaces (APIs), cloud platforms, and internal systems makes them ripe for exploitation. Prompt injection attacks and manipulated outputs are not hypothetical; they’re active threat vectors. If an agent has the authority to write code, send emails, or make purchases, a compromised prompt can become a full-blown breach.

EPIC is advising clients to review their Cyber Liability and Contingent Business Interruption coverage in light of these expanded digital risks. It’s not just about securing the AI, it’s about understanding how it connects across your enterprise.

Boardroom Blind Spots

As AI agents enter legal, finance, HR, and operations, board-level oversight must evolve. Governance structures designed for human error do not easily apply to algorithmic behavior. Boards must ensure:

• AI usage policies are clearly defined
• Model explainability is validated
• AI risk audits are regular and documented

We see D&O carriers beginning to ask questions around AI risk posture during renewals. Passive deployment without governance is becoming a boardroom liability.

Insurance Innovation: The Market Isn’t Ready

Current policies were not built for autonomous decision-making systems. Traditional Tech E&O, Cyber, and Product Liability products assume human oversight. But in an AI-first workflow, where tasks are completed without review, coverage clarity collapses.

Some carriers are piloting AI-specific endorsements or new products around “algorithmic accountability” and usage-based AI risk. But the market is nascent. Brokers must help clients:

• Map their AI stack and identify exposure points
• Evaluate coverage gaps in standard forms
• Push for manuscript language where necessary

Strategic Risk or Strategic Advantage?

The adoption of AI agents is not a binary risk. It is a spectrum. Organizations that treat agents like any other tool will miss the point-and likely the exposure. Those who treat them like junior executives, with onboarding, supervision, and KPIs, are already ahead.

The question is not whether to use AI agents. It’s whether your organization is architected to absorb their impact.

What happens when your procurement agent issues a flawed contract, or your financial bot rebalances your portfolio using flawed data? What if the logic is sound, but the training data is biased? What if the vendor updates the model overnight, and behavior changes by morning?

Looking Ahead: Agentic Autonomy at Scale

By 2030, AI agents will be embedded in logistics chains, construction sites, hospital networks, and legal teams. These systems will negotiate, execute, and assess without pause. The line between tool and teammate will blur entirely.

That future doesn’t have to be dystopian. But it does demand vigilance. We are on the verge of the greatest shift in operational liability since the invention of the spreadsheet.