Agentic AI in customer service can pursue a goal by choosing and carrying out multiple steps—not merely drafting a reply or suggesting what an employee should do. That ability can support tasks such as answering customer questions, summarizing calls, and moving selected service workflows forward. It also makes permissions, human oversight, and auditability central: an agent allowed to retrieve information presents a different level of risk from one allowed to change a customer record, send a message, or settle a claim.
Evidence from insurance illustrates both the possibilities and the limits. In its 2025 survey, the European Insurance and Occupational Pensions Authority (EIOPA) found examples across customer service and back-office work, but most reported customer-facing agentic use cases were still proofs of concept. Those findings describe surveyed insurers, not customer-service organizations as a whole.
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What agentic AI means in customer service
An agentic customer-service system is given a goal and can make decisions and take multiple actions toward it, with limited or no human intervention. A customer request might require the system to identify intent, retrieve approved information, consult a record, and prepare or perform a next step. The defining distinction is not that the system uses generative AI; it is the degree to which it can act.
A generative assistant may draft an answer for an employee or recommend a next step. An agent may also execute steps through connected tools. In practice, “agentic” does not describe one fixed level of autonomy: a system that only searches approved material has much less authority than one that can update records, send communications, or make consequential decisions. EIOPA and the National Cybersecurity Center of Excellence (NCCoE) both make oversight and authority important considerations for these systems.
Think in terms of authority, not just conversation quality
| Capability | What the system can do | Why the boundary matters |
|---|---|---|
| Retrieve information | Search an approved knowledge source or customer record and return relevant details. | Access should be limited to the information needed for the task, and answers still need to be checked for accuracy. |
| Prepare an action | Draft a reply, summarize a conversation, or propose a record change for an employee to review. | A human can correct the output before it affects a customer or a record. |
| Execute a routine action | Send an approved response or make a narrowly defined update through a connected workflow. | The system needs clear authorization, reliable logging, and a way to stop or hand off when the case falls outside its rules. |
| Resolve a consequential case | Make or carry out a decision that changes a customer’s position, such as settling a claim. | Errors, unfair outcomes, or unclear accountability can have greater consequences; human review and carefully bounded authority become especially important. |
These are practical levels for evaluating a service design, not a formal classification scheme issued by EIOPA or NIST. The same product can operate at different levels depending on its tools, permissions, workflow, and oversight.
What agentic AI can do in customer service
EIOPA’s Generative AI Market Survey: Outlook, Use Cases and Risk Management reports on a July 2025 survey of insurance undertakings. Its examples are useful evidence of possible service workflows in a regulated sector, but they should not be treated as established deployment patterns for every industry.
Assist customers through chat and voice
Reported customer-facing examples include chatbots and voicebots. A system can use a customer’s question to identify an intent, retrieve relevant information, and respond through a service channel. EIOPA also describes a production example of a GenAI chatbot providing information about claim compensation. That example shows information delivery; it does not establish that the chatbot independently decided a claim.
Summarize service conversations
Call summarization can turn a conversation into a record for later service work. EIOPA describes production examples in which systems summarized customer calls into conversation histories. Summaries may help agents review prior contact, but the record remains dependent on the system capturing key details correctly; a summary should not silently replace the underlying conversation where exact wording or context matters.
Personalize customer communications
EIOPA reports examples of personalized advertising banners, including banners shown in a customer portal. This is a customer-facing use of generated or selected content, but it is not the same as resolving a customer-service request. Personalization also raises questions about the data used, the appropriateness of targeting, and the customer’s expectations.
Handle email and organize incoming requests
Back-office examples in the survey include recognizing query intent, assessing invoices, and generating structured data from insurance contracts for upload into a customer relationship management (CRM) system. EIOPA also reported one insurer’s plan to automate responses to more than 350,000 customer emails. That figure describes a reported plan, not a verified completed automation result. Likewise, a capability to generate or upload structured data does not by itself establish that every entry can safely bypass review.
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- Record all incoming calls needing service
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Move selected claims and application workflows forward
Reported examples include automated processing and settlement of low-value claims, fixing errors in submitted applications, and producing information about claim compensation. These cases can involve more than answering a question: the system may need to gather data, detect a missing or inconsistent item, and trigger a workflow. When a workflow can affect eligibility, payment, or another consequential outcome, the difference between preparing a recommendation and making the decision should be explicit.
Support quality review and operational work
EIOPA’s back-office examples also include auditing service calls. Used as a review aid, an agent could identify interactions for closer examination or organize findings for a human reviewer. Such assistance should not be mistaken for a complete or inherently fair assessment of employee performance; the reliability and interpretation of the review output matter.
What the adoption evidence does—and does not—show
EIOPA reported 957 GenAI use cases in its survey, of which 84 were labeled agentic AI use cases. Those 84 were at different stages of development and maturity, so they should not be described as 84 production deployments. Of the 84, 49 were customer-facing and 35 back-office; EIOPA says customer-facing examples were mostly proofs of concept.
EIOPA also reported that 40% of surveyed insurance undertakings were already using GenAI in customer service. That is a GenAI customer-service figure for surveyed insurers, not an agentic-AI adoption rate and not a rate for all businesses. Separately, 65% of surveyed insurance undertakings were actively using GenAI and another 23% planned to implement it within three years. These organization-level figures concern GenAI broadly, not verified deployment of autonomous customer-service agents.
The distinction is important: broad experimentation or use of generative AI does not tell a business how many organizations have agents with permission to carry out customer-affecting actions. Nor does a proof of concept establish that a system is reliable, cost-effective, or ready for production.
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Potential benefits—and what has not been demonstrated
EIOPA’s survey respondents expected GenAI to enable faster, more personalized customer experiences and to contribute to operational efficiency, cost reduction, and productivity gains for insurers. These are expected benefits reported in a particular sector survey. The findings do not establish that those benefits were consistently achieved or quantify a causal business impact.
Agentic workflows may be useful where a request involves repeatable steps, information is available from approved sources, and the system can operate within clearly defined limits. For instance, summarizing a call or routing a request for review is different from granting a refund or settling a claim. The potential value of automation needs to be weighed against the work required to integrate systems, oversee outcomes, correct mistakes, and handle exceptions.
A vendor example illustrates the kinds of controls some enterprise platforms describe, not independent proof of results. Netomi’s Microsoft Marketplace listing describes autonomous and human-guided customer-experience workflows, orchestration across channels, confidence scoring, fallback logic, live audit trails, and observability. These are vendor-provided descriptions; they do not demonstrate comparative performance or establish that a specific deployment will deliver a particular outcome.
Risks that grow with an agent’s authority
Incorrect answers and hallucinations
EIOPA identifies hallucinations as a GenAI risk. An inaccurate response can mislead a customer; an inaccurate action can also alter a record, send an incorrect communication, or move a workflow in the wrong direction. Confidence scores or fluent wording do not, on their own, prove an answer is correct. Systems should have a defined response when they cannot find reliable support for an answer.
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Customer service interactions can involve personal information. EIOPA names data protection as a risk area, and the more systems an agent can access, the more important it is to limit that access to the task. Teams should consider what customer data is exposed to the model or connected tools, whether the agent can retrieve information about the wrong person, and how generated summaries and records are retained and reviewed.
Cybersecurity, prompt injection, and tool misuse
EIOPA identifies cybersecurity concerns. NIST NCCoE’s 2026 concept paper raises direct and indirect prompt injection as issues for agentic systems: instructions embedded in a customer message or other content may try to redirect an agent or induce it to misuse its tools. A system that can read untrusted content and act through connected services needs controls that distinguish data to process from instructions it is authorized to follow.
Identity, authorization, and delegated access
NIST NCCoE frames agent identity and authorization as open security challenges. Its concept paper raises questions about least-privilege permissions, how authorization should change as context changes, how an agent can prove it is authorized for a particular action, and how “on behalf of” access should be tied to a human authorization. These are especially relevant when an agent uses the same business systems as employees: access should not be broader merely because a workflow is automated.
Explainability, traceability, and fair treatment
EIOPA highlights explainability, traceability, non-discrimination, reliability, and trust as concerns for agentic systems. If a customer asks why an action was taken, the business needs enough information to understand the decision path and identify which data or rule mattered. If a system handles similar customers differently, teams need a way to detect and investigate that disparity rather than relying on the generated explanation alone.
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When an agent takes several steps, it can be difficult to identify where a mistake arose unless actions and intent are recorded. Customers also need a reliable path to a human when the system cannot solve the issue or the case requires judgment. EIOPA warns that fully autonomous systems in core areas without human oversight pose significant risks. Human involvement is particularly important where a decision has material customer consequences.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to set boundaries and oversight
Safeguards should match the agent’s available actions. The following controls translate the risks raised by EIOPA and NIST into operational questions for a customer-service deployment; they are practical recommendations, not a universal design prescribed by either source.
- Define the task and prohibited outcomes. State what the agent is meant to accomplish, what cases it must not handle, and what it may say or do when information is missing or a request is outside scope.
- Grant the minimum access needed. Restrict data, systems, and tools to the specific workflow. Separate permission to read a record from permission to alter it, send a customer message, or complete a consequential action.
- Require approval for consequential actions. Decide in advance which actions can run automatically and which require a human to review or authorize them. Approval should apply to the action itself, not merely the text the agent generated before taking it.
- Record actions and intent. Keep tamper-resistant, reviewable logs that show which identity acted, what information or tool was used, what action was taken, and under whose authority. NIST NCCoE identifies auditability and binding agent actions to human authorization as issues to address.
- Make handoff dependable. Provide a clear route to a human when confidence is low, a tool fails, the customer asks for escalation, or the case meets an exception condition. Preserve enough conversation and workflow context for the employee to continue without making the customer start over.
- Test failures and changing context. Test how the agent responds to conflicting or malicious instructions, missing data, access denial, duplicate requests, and interrupted workflows. Reassess whether its authorization remains appropriate when the customer, task, or risk context changes.
- Monitor outcomes after launch. Review accuracy, exceptions, customer-impacting actions, handoffs, and suspected unfair treatment. Observability is useful only if a team can interpret what happened and intervene when the workflow goes wrong.
How to evaluate an agentic customer-service option
There is no head-to-head product evidence in the material available here, so a vendor ranking would not be justified. Compare an option against the actual service task rather than its label or a promise of autonomy.
- Task and channel scope: Which customer-service tasks and channels does it support, and are these capabilities available in the configuration being considered?
- Action permissions: Can it retrieve information, draft content, send messages, update records, or resolve cases? Which actions can be limited or require approval?
- Knowledge and record integration: Can it use approved customer-service information and the relevant customer records without exposing unrelated data?
- Human review and handoff: Which cases trigger review, and does the handoff include the conversation and actions already taken?
- Audit trail and observability: Can a reviewer reconstruct what the agent did, what it relied on, and what authority it used?
- Fallback behavior: What happens when the agent is uncertain, a connected system is unavailable, or a workflow cannot be completed?
- Evidence for the intended use: Distinguish a vendor’s feature description, a proof of concept, and a production example from independently established evidence of reliable outcomes in the workflow you need.
Frequently Asked Questions
Does agentic AI always work without human involvement?
No. “Agentic” describes the system’s ability to pursue a goal through decisions and actions; it does not require fully autonomous operation. A deployment can limit the agent to preparing work for approval or require human review at specific steps.
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Not necessarily. A chatbot is an interface for conversation, while agentic describes how much the system can decide and do. A chatbot can provide information without taking actions, and a conversational interface can also be connected to workflows that perform actions.
Does EIOPA’s survey show that agentic customer service is widely deployed?
No. EIOPA’s 2025 survey found 84 use cases labeled agentic among 957 reported GenAI use cases, at different stages of maturity; customer-facing agentic examples were mostly proofs of concept. The figures are from surveyed insurance undertakings and do not measure adoption across all sectors.
Should an agent be allowed to settle claims or make other consequential decisions?
The cited evidence does not establish a universal rule for every organization or claim type. EIOPA warns of significant risks when fully autonomous systems operate in core areas without human oversight. Any consequential authority therefore calls for explicit limits, appropriate human review, and a record of the agent’s actions and authorization.
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