There is a new risk category.
Agentic AI Risk emerges when AI systems can plan, act, use tools, access enterprise systems, influence customers, affect legal or financial outcomes, or operate across vendors and portfolios.
Law firms need to advise on it. Consultancies need to assess it. Enterprises need to govern it. Carriers and reinsurers need to underwrite and aggregate it. AgenticFides builds the product infrastructure for all of them.
Existing AI policies, model-governance programs, cybersecurity controls, and professional-liability frameworks remain important. They are not enough, on their own, to evaluate a deployed agentic capability in practice.
Common Agentic AI Risk pathways.
i.
Autonomous decision failure
Faulty operational, financial, customer, claims, pricing, or triage decisions.
ii.
Unauthorized tool or workflow execution
Tool, API, workflow, or permission use outside the intended envelope.
iii.
Data, privacy, and confidentiality loss
Improper access, disclosure, transfer, retention, or use of sensitive information.
iv.
Manipulation and adversarial misuse
Prompt injection, poisoned context, retrieval manipulation, or social engineering.
v.
Faulty delegation of authority
Agents granted, assuming, or exercising authority beyond the intended mandate.
vi.
Multi-agent or vendor cascade
Interacting agents, tools, models, or vendors amplifying errors or dependencies.
vii.
Regulatory, contractual, or claims error
Wrong notices, commitments, rights determinations, coverage statements, or legal positions.
viii.
Operational resilience failure
Unreliable performance, weak fallback, failed recovery, or disrupted business services.
ix.
Portfolio accumulation and concentration
Correlated exposure across model families, vendors, tools, jurisdictions, or business functions.