Even with the digitalization of customer service, the phone remains relevant in more complex situations. According to data from the Customer Service Operations Survey 2025, by Gartner, 61% of customers still prefer this channel to resolve this type of demand.
Voice AI agents expand what can be resolved during these interactions. They understand requests in natural language, consult company information and, when connected to other systems, can perform actions throughout the call.
In large operations, this can mean more demands resolved during the contact itself, transfers with more context, greater capacity to absorb volume, and more consistent use of corporate knowledge.
The greater the participation of these agents in service, the more relevant become the decisions about which information they can access, which actions they can perform, and which controls need to accompany this performance.
What do voice AI agents change in operations?
A voice AI agent can track different stages of a request during the call itself.
When connected to the company's databases and systems, it can understand the request, retrieve the necessary information, and execute actions within the permissions defined for that journey.
In practice, this model can bring impacts such as:
- More requests resolved during the call: the agent can consult data, check requests, update records, or execute steps of a process;
- Greater capacity to absorb volume: multiple interactions can be conducted simultaneously, supporting operations subject to call spikes, campaigns, incidents, or seasonality;
- Transfers with more context: when a person needs to take over the service, the conversation history, collected information, and steps already performed can accompany the call;
- More consistent use of corporate knowledge: different agents can consult the same policies, rules, documents, and product information during interactions.
This last point also increases the importance of the quality of sources. An outdated policy or contradictory information can influence the behavior of multiple agents and reach a large volume of customers.
Therefore, the impact of voice AI is directly linked to three elements: what the agent knows, which systems it can access, and which actions it is authorized to perform.
What does a company need to structure to operate voice AI agents?
As agents take on more steps of the service, different parts of the operation need to work in a coordinated manner. Below are the main points that need to be structured to support voice AI agents in production.
Knowledge base
The first point is to define which information will be available to the agents. The company can gather FAQs, policies, internal documents, manuals, business rules, and product information in the sources used by the agents.
Thus, during the conversation, RAG (Retrieval-Augmented Generation) helps locate relevant content in these bases and use it to build responses.
When multiple agents use this knowledge, it is also necessary to organize which information serves each journey. This involves defining which sources each agent can consult, who is responsible for updating them, and how new versions will be incorporated.
This organization helps keep the knowledge used by the AI aligned with the company's current rules and processes.
Integration with systems
With the appropriate information available, it is necessary to connect the agent to the systems involved in the service. These integrations expand its operational capacity by allowing queries, updates, and execution of steps during the call.
An agent connected to the CRM, for example, can query specific customer information during the call. With access to APIs, service platforms, or other internal systems, it can also update records, open requests, or perform actions defined in that service flow.
The company can use these integrations to enable actions such as:
- Query customer data;
- Check the status of a request;
- Update registration information;
- Register a new request or ticket;
- Check the status of an order;
- Forward the demand to the responsible area.
This access needs to be configured according to the criticality of each action, especially when the agent handles sensitive data or changes information in the company's systems.
Performance and latency
Performance and latency are other important points for maintaining the fluidity of voice conversations.
Before responding, the agent may need to interpret the request, search for information in company databases, and consult systems such as CRM or internal platforms. The more queries are part of the journey, the greater the attention needed to response time.
If this processing causes long pauses, the customer may repeat the question, interrupt the agent's speech, or interpret the silence as a service failure.
Therefore, the company needs to especially monitor journeys that involve multiple queries and integrations, ensuring that the increase in agent capacity preserves the flow of the conversation.
Security and data protection
Access to corporate systems can bring agents into contact with personal data, financial information, service histories, and internal organizational content.
In this scenario, the solution design needs to consider controls such as access management, data masking, definition of processing environments, and protection of the information flow.
For companies operating in regulated sectors, it is also necessary to consider specific security and data protection requirements. These requirements can influence the adopted architecture, with the use of cloud, hybrid, multicloud, or on-premises environments, according to the needs of the operation.
Agent governance
With multiple agents in production, the company needs to maintain visibility over what is operating.
This includes knowing which agents are active, which sources they use, which systems they access, which actions they can perform, and which configurations are published.
Changes made over time also need to be tracked. Changes to prompts, knowledge bases, permissions, or parameters can modify agent behavior during interactions.
Therefore, features such as change history, version control, authorship identification, testing, and monitoring help keep the operation under control.
Finally, governance creates a foundation to evolve agents more safely, incorporating new use cases and capabilities as the company monitors their behavior in production.
How to define the autonomy of voice AI agents?
The level of autonomy can vary depending on the type of activity performed by the agent.
In a journey of frequent questions, for example, it can consult information and guide the customer. In processes involving updating records, accessing sensitive data, or transactions, the company needs to establish more rigorous permissions and criteria.
This difference is related to the impact that each action can generate for the customer and for the operation itself. The higher the criticality, the more important it is to define which activities the agent can perform alone and which require additional validations.
Among the factors that can guide this decision are:
- Criticality of the action;
- Data sensitivity;
- Value involved;
- Confidence in the interpretation;
- Internal rules;
- Regulatory requirements.
These same criteria help define when human assistance should take over. The company can define the transfer when the agent cannot safely understand the request, identifies signs of urgency, or encounters a situation that requires judgment.
When this happens, the context accumulated during the conversation can accompany the call. The agent receives the collected information and can continue the interaction from the point already reached.
Furthermore, limits can evolve. An agent initially responsible for triage can take on new functions as results, integrations, and operational controls mature.
Thus, autonomy must reflect the role of each agent, the impact of their actions, and the criteria defined for human intervention.
How to measure the impact of AI voice agents?
The choice of metrics depends on the objective of each use case.
If the company seeks to reduce the volume directed to human teams, it can track how many calls remain with the agent. If the focus is on resolution, it also needs to observe how many requests are completed during the AI service itself.
Other indicators help complete this analysis:
- Resolution rate;
- Call retention;
- Contact recurrence;
- Rate and reason for transfers to human support;
- Average response time;
- Comprehension failures;
- Customer satisfaction;
- Operation productivity.
These numbers gain more value when analyzed together.
A high retention rate, for example, must be accompanied by the resolution of requests. Likewise, a reduction in transfers to human attendants must be analyzed together with the quality of service and the criteria defined for that transfer to occur.
It is also important to monitor what happens when the agent receives new functions, such as access to more systems or authorization to perform new actions during the call. These changes can increase the number of requests resolved by the AI, but they also require attention to errors, response time, and the security of the actions performed.
Therefore, measurement needs to show how much the agent can resolve, with what quality, and what effects this performance generates for the operation.
How does Nava apply voice AI agents in operations?
Nava One is our Conversational AI platform for voice operations. The solution connects specialized agents to the company's knowledge and systems to conduct interactions in natural language and resolve different stages of service during the call itself.
In practice, Nava One allows:
- Configure specialized agents for different journeys and operational needs;
- Consult information and execute actions in the company's systems, according to the access and permissions defined for each agent;
- Define levels of autonomy and transfer criteria for human assistance;
- Preserve the conversation context during the transfer, forwarding the history and information already collected;
- Configure, test, and monitor agents in an administrative console, with traceability of the changes made.
During the call, Nava One processes the customer's speech, consults the necessary information, and returns the response in voice, with latency below 1.1 seconds. This helps preserve the pace of the conversation even when the agent needs to access knowledge or other systems before responding.
The platform can also adapt to different infrastructure models, including hybrid environments, multicloud and on-premises. For operations that handle sensitive information, masking features help protect data during processing.
With this structure, Nava One allows you to expand the performance of voice agents while maintaining control over knowledge, autonomy, integration, and security.
Learn about Nava One and see how to expand the resolution capacity of your service operation.
Frequently asked questions about voice AI agents
What is a voice AI agent?
A voice AI agent is a system capable of understanding what a person says, interpreting context and intent, retrieving information, and generating responses during a voice interaction. When connected to company systems, it can also perform actions within the permissions defined for that journey.
What is the difference between a voice AI agent and an IVR?
The IVR guides the customer through predefined menus and flows. The voice AI agent interprets natural language requests and conducts the interaction based on context, available information, and rules configured by the company.
What can a voice AI agent do during a call?
The possibilities depend on the available integrations and permissions. The agent can answer questions, consult information, collect data, update records, execute process steps, and transfer the interaction to a human attendant.
How to define the level of autonomy of a voice AI agent?
The company may consider the impact of the action, the data involved, the level of confidence in the interpretation, the process rules, and the regulatory requirements. These criteria help define which activities the agent can perform and which situations require human intervention.
When should an agent transfer the call to a person?
The transfer can be triggered by criteria such as low confidence in interpretation, successive comprehension failures, signs of urgency, type of request, or the need for human judgment. The history and information collected by the agent can accompany this transfer.
What is needed to escalate voice AI agents?
An operation at scale requires reliable knowledge bases, integration with systems, adequate performance, security, and governance over agents, access, and configurations. These elements help expand use cases while maintaining control over the operation.
How to measure the results of voice AI agents?
Indicators such as resolution, call retention, overflow, contact recurrence, response time, satisfaction, and productivity help evaluate results. The joint analysis of these metrics shows how much the agent can resolve and what effects their performance generates for the operation and for the customer experience.
