On 22 July 2026, OpenAI introduced Presence, a deployed product for eligible enterprise customers that brings AI agents into internal and customer-service workflows. The notable point is not that there is another chatbot. The announcement emphasizes policies, guardrails, simulations, quality grading and escalation to people. This is a clear signal that the competitive measure is shifting from answering ability to the capacity to operate services reliably.
Over the past few years, many organizations have experimented with AI through question-answer assistants, content generation or employee information search. These uses create value, but they often remain at the interaction layer. Once an agent can access systems, perform approved actions or handle part of a customer case, the management question changes completely. The organization no longer needs only a correct answer. It needs to know whether the agent follows the right process, recognizes situations where it must stop, leaves an auditable trace and hands work to the right person.
A product announcement, but a change in perspective
According to OpenAI’s announcement, Presence is designed for work such as answering questions, resolving service requests, supporting insurance claims and handling internal IT requests. Before deployment, implementations can be tested against common requests, edge cases and higher-risk scenarios; evaluation can assess outcomes, policy compliance, tool use and escalation decisions. Once in operation, sessions, escalations and quality signals are used to identify where the agent performs well or needs adjustment.
This announcement should not be read as evidence that every organization is ready to replace people with agents. OpenAI also states that Presence is available to eligible enterprise customers through a limited general-availability program, with deployments led by forward-deployed engineers and selected partners. It is nevertheless a useful example of market direction: AI creates more value when it sits within a system of process, accountability and feedback, rather than operating as a stand-alone answering tool.
From chatbot to service capability
A traditional chatbot is evaluated primarily by its ability to understand a question, respond quickly and maintain natural conversation. Those criteria remain necessary, but they are not sufficient for an actor participating in a service process. In operations, a response that sounds plausible may still cause harm if the agent gives the wrong refund condition, misreads a policy, performs an unauthorized action or fails to recognize a sensitive complaint.
Service capability should therefore be understood more broadly than conversational quality. It includes the ability to identify intent, retrieve the right data from authorized sources, apply the right rules to each case, perform or propose actions within delegated authority, and hand work to an appropriate person when confidence or risk exceeds a threshold. Each stage has to be designed and measured. A good agent is not the one that automates the most; it is the one that completes the assigned work correctly and stops at the right time.
Management example
Consider a customer-support agent for a retailer. It can look up order status, explain return policy and propose a new delivery time. But a refund request above a value threshold, signs that a customer’s payment information has been exposed, or a repeated complaint should be handed to an employee. Good design does not treat escalation as failure; it is a mechanism that protects the customer, the employee and the brand.
Four layers of control before scaling
The first is control over work scope. The organization needs to specify which requests the agent may handle, which systems it may read or write, and which actions always require human approval. The second is control over knowledge and data. Responses must be grounded in verified sources with versions, owners and updating rules; without this, the system can easily answer from outdated or conflicting information.
The third is control over decisions and escalation. The organization needs thresholds for what the agent may do autonomously, what requires confirmation and what must be handed off immediately. Thresholds should not rely only on model confidence; they should also reflect potential customer, legal, financial and reputational impact. The fourth is post-operation control. Interaction logs, escalation rate, process errors, resolution time, customer feedback and employee correction effort should be treated as management data, not merely technical data.
| Control layer | Management question | Useful indicator |
|---|---|---|
| Work scope | What may the agent do, and what may it not do? | Out-of-scope requests, blocked actions. |
| Knowledge and data | Which sources are allowed, and who owns updates? | Content freshness, retrieval errors, answers without a source. |
| Decision and escalation | When should the agent act, seek confirmation or hand off? | Correct handoff rate, late handoff rate, exception-resolution time. |
| Learning after operation | Which errors are fixed, and how are they tested again? | Repeated errors, time to remediation, pre-release test results. |
Do not measure only conversation volume
Measuring usage volume or automation rate may create a sense of rapid progress, but it does not answer the central question: is the organization serving customers and operating better? An agent may handle many conversations, yet real value may fall if customers need to contact the company again, employees spend substantial time correcting outputs or risky cases are escalated too late.
The metric set should connect efficiency and quality. In customer service, useful measures include first-contact resolution, time to a final decision, appropriate escalation rate, post-interaction satisfaction and repeat-contact rate. For internal support, organizations should add time saved after subtracting verification effort, process-compliance quality and changes in workload across specialist teams. These measures help avoid confusing more activity with more value.
Implications for Vietnamese businesses
For Vietnamese businesses, an appropriate starting point does not necessarily mean buying a large platform or trying to automate an entire contact center. They can begin with one task that has enough volume, relatively clear rules, data with a named owner and controllable risk. Examples include order-status lookup, helping employees find internal procedures or classifying incoming requests. Before expanding, the organization should build a test set containing normal, exceptional and sensitive cases.
The hardest task is often not building the agent, but agreeing on who owns policy, data, handoff decisions and error correction. Without a process owner and a cross-functional review rhythm, an agent will quickly reproduce the organization’s existing disorder. Conversely, when AI deployment is treated as an opportunity to clarify processes and standardize knowledge, the organization can improve both customer experience and operating capability.
Conclusion
Presence is a product announcement, but its wider management message matters: enterprise AI is moving from intelligent answers to accountable work. Organizations that wish to benefit from the agent wave need to design scope, data, action rights, escalation and the learning loop after operation. Technology creates a new capability; the management system determines whether that capability becomes lasting value.
References
OpenAI. (2026, July 22). Introducing OpenAI Presence. https://openai.com/index/introducing-openai-presence/
World Economic Forum. (2026, March 16). Organizational transformation in the age of AI: How organizations maximize AI’s potential. https://www.weforum.org/publications/organizational-transformation-in-the-age-of-ai-how-organizations-maximize-ais-potential/
