Autonomy
Agentic AI
Also called: AI agents, autonomous agents
Definition
Agentic AI describes systems that plan multi-step work, call tools and external systems, and take actions on their own rather than only returning text to a person.
The distinction that matters is not intelligence but consequence. A model that drafts an email is a suggestion engine; a model that sends the email, updates the claim file, and issues a payment has been delegated authority. Agentic systems are defined by the actions they are permitted to take and the point at which a human still has to agree. The engineering that makes this possible — planning, memory, tool calls — is now widely available, which means the authority question, not the capability question, is what separates a well-run deployment from an incident.
Capability is not the hard part
Planning loops, tool calls, and retrieval are commodity engineering now; most vendors and open-source frameworks can wire an agent together in an afternoon. What is hard is deciding which actions an agent may take unsupervised, what it must escalate, and how a wrong action gets caught and reversed before it reaches a customer or a ledger. Organizations that treat agent projects as engineering exercises consistently under-invest in this second question, then discover its cost the first time an agent does something technically correct but operationally wrong. The build is the easy quarter; the authority design is the one that determines whether the system is trustworthy.
In insurance operations
An agent in claims is not a chatbot with better manners. It participates in a decision that carries reserve, regulatory, and reputational consequence, which means its authority has to be scoped as deliberately as a human adjuster's settlement limit. That scoping should be written down in the same terms an adjuster's authority is written down: dollar thresholds, required evidence, and lines of business it may not touch without escalation. Skipping this step because the system is 'just handling the easy cases' is how an agent ends up quietly making a decision nobody agreed it should be allowed to make.
What changes when an agent can act
Once a system can act rather than just suggest, the review process has to move earlier. A wrong recommendation from a copilot is caught when a person reads it; a wrong action from an agent is caught only if something downstream is built to catch it, which means monitoring, reversal paths, and audit trails become part of the product rather than an afterthought. Organizations that succeed with agentic deployments generally build the monitoring and rollback capability before they expand the agent's authority, not after an incident forces the question. That ordering is the single best predictor of whether an agent program stays governable as it scales.
In practice
- Write the action list before the model choice: what may it do, and to what value
- Define the reversal path for every irreversible action
- Log the reasoning trace alongside the action, not just the outcome
- Build monitoring and rollback before expanding the agent's authority, not after an incident
