Managing Agentic AI in Real‑World Use: From Outputs to Actions
Introduction
Agentic artificial intelligence (AI) represents the next frontier for organizations deploying AI systems. Unlike traditional AI that only provides information, agentic AI can select and execute actions on a user’s behalf based on instructions, context, and configured permissions. This capability introduces new legal, governance, and risk‑management challenges that must be addressed proactively.
Defining Agentic AI
According to NIST’s CAISI (January 2026 Request for Information on AI agent security), an AI agent system is “capable of taking autonomous actions that impact real‑world systems or environments,” typically combining a generative model with tools that enable it to act. Examples include drafting and sending emails, updating records, submitting filings, and initiating refunds or credits.
Key Risks and Management Practices
1. Human Oversight Is Essential
Even when AI systems automate actions, human supervision remains critical, especially for high‑impact activities such as consumer‑facing decisions, financial exposures, or handling sensitive data. Structured “stop points”—where a person reviews and approves actions—help mitigate legal and regulatory risks. Oversight requirements are reflected in frameworks such as the OECD AI Principles and the NIST AI RMF, as well as emerging regulations like California’s updated privacy law and the EU AI Act.
2. Governance Gaps and Policy Alignment
Many existing AI policies focus on generative outputs rather than agentic capabilities. Organizations must shift the question from “Is the output acceptable?” to “What is the system allowed to do, and under what conditions?” Tailored pre‑deployment risk assessments, clear internal ownership, and explicit accountability structures are essential to prevent responsibility dilution across teams.
3. Monitoring for Malicious Activity and Cascading Errors
Automated execution can propagate small mistakes rapidly. New security threats, such as “agent hijacking” (a form of indirect prompt injection), require robust monitoring, logging, and the ability to pause or disable agents quickly. Leveraging AI‑driven detection can further enhance anomaly identification and demonstrate responsible stewardship.
4. Consumer Interaction and Existing Legal Frameworks
When AI agents engage consumers—e.g., via personalized texting—organizations must consider consumer‑protection laws like the Telephone Consumer Protection Act and state‑specific regulations. Early identification of legal risks, compliance planning, and system design that respects existing federal, state, and local statutes are vital.
Practical Strategies Before Deployment
To address the identified risks, organizations should implement the following practices:
- Pre‑deployment risk assessment: Tailor assessments to the agent’s purpose, system access, data exposure, and foreseeable misuse scenarios (e.g., prompt injection).
- Scope and authority definition: Document goals, allowed tools, data sources, prohibited actions, and oversight gates (human approval points, escalation triggers).
- Clear internal ownership: Assign an accountable owner for the agent’s behavior, performance, and monitoring, with authority to seek legal and compliance review.
- Technical controls: Implement monitoring, audit logs, and a tested ability to pause/disable the agent and revoke credentials.
- Data inventory and review: Continuously assess privacy, confidentiality, security, and data minimization for all datasets the agent can access.
- Contractual safeguards: Review third‑party terms, audit rights, security requirements, and liability allocations when agents interact with external data or services.
- Cross‑functional governance: Ensure input from business, technical, legal, and compliance stakeholders to create a balanced oversight structure.
Conclusion
Agentic AI offers powerful automation capabilities, but its ability to act autonomously across systems amplifies risk. By establishing robust human oversight, aligning governance structures, monitoring for malicious activity, and integrating legal compliance into the deployment lifecycle, organizations can responsibly harness the benefits of agentic AI while mitigating potential harms.
