Industry
4 Best Practices for Managing an AI Agent in Enterprise CX
Harry Yu
Deploying an AI agent is only the beginning.
For enterprises, the real challenge starts after the agent goes live. Customer issues change, products evolve, policies are updated, and customer behaviour rarely follows the scenarios defined during implementation. An AI agent that performs well at launch can quickly lose accuracy if it is not continuously managed and improved.
This is why running an AI agent in production requires a different discipline from simply building one. After two years of deploying AI agents across e-commerce, fintech, mobility and consumer electronics, we have identified four practices that are particularly important for enterprise CX teams.
1. Design the intent taxonomy when deploying the agent
One of the biggest sources of risk for an enterprise CX agent is misunderstanding customer intent.
When an agent interprets a customer's issue incorrectly, every subsequent step can be affected. It may retrieve the wrong knowledge, apply the wrong policy or trigger the wrong workflow. In customer experience, this challenge is amplified by the fact that customers rarely describe their problems in the structured language used by the business.
The solution is not simply to keep adding more intents as new customer issues appear. Many AI agent platforms build their intent taxonomy bottom-up. New intents are added as new types of tickets emerge. While this approach seems practical initially, it can create increasing overlap between intents. As the taxonomy grows, similar issues become harder to distinguish, and the problem is often discovered only when customers are incorrectly routed in production.
A better approach is to design the intent taxonomy top-down before deployment. The objective is to map the underlying customer issues and make the intents as mutually exclusive and collectively exhaustive as possible.
In our production deployments, we have not encountered a consumer business unit with more than 100 distinct underlying customer issues. When the number of intents starts to grow significantly beyond that, it is worth examining whether the taxonomy itself has become unnecessarily complex.
For issues that are easily confused, dedicated routing playbooks can also be used to explicitly distinguish between them. For example, a customer who is not interested in an offer and a customer who has rejected an offer may use very similar language, while requiring different responses or workflows. These distinctions need to be deliberately designed rather than left entirely to the model.
A well-designed intent taxonomy also makes the agent easier to test. With a defined universe of issues, enterprises can systematically evaluate whether similar intents are being distinguished accurately. Whenever a new issue is introduced, it can be tested against the existing intent rather than being evaluated in isolation.
The goal is to create a reliable mapping between the enormous variety of customer language and a manageable intent taxonomy.
2. Give AgentOps ownership and the tools to measure performance
Building an AI agent and operating an AI agent are two different disciplines.
Once an agent is live, someone needs to monitor its performance, identify emerging issues, refine workflows, manage new intents and keep the agent aligned with the organisation's policies. This is where AgentOps becomes important.
AgentOps is the operational function responsible for managing an AI agent throughout its lifecycle. Much like IT operations manages enterprise systems after deployment, AgentOps ensures that the agent continues to perform as the business changes.
The difference is speed. AgentOps needs to respond much faster than traditional software development cycles. A product launch may require a new intent tomorrow. A policy change may require a workflow update immediately. Routing every operational change through a lengthy engineering process makes it difficult to manage the agent at the pace of the business.
But flexibility cannot come at the expense of accuracy. Enterprises often require AI agents to operate at 99.9% accuracy or higher. At that level, simply knowing whether an interaction succeeded is not enough. AgentOps needs to understand exactly where and why an agent succeeded or failed.
This requires visibility into the information available to the agent, the parameters and rules applied, the decision it made and the action it ultimately took. Without that level of measurement, AgentOps can change the agent, but cannot confidently determine whether the change actually made it better.
3. Put deterministic controls around critical decisions
LLMs are powerful because they handle ambiguity well. But enterprise CX also contains decisions where ambiguity is precisely what you want to eliminate.
For these cases, a useful design principle is: use LLMs heavily offline and use deterministic code heavily online.
Whether it is engineering hours or LLM inference cycles, spend them upfront. Test logic offline. Evaluate edge cases offline. Generate and validate automation scripts offline. At runtime, use deterministic systems wherever the outcome can be explicitly defined.
A few best practices:
1. Do not allow an LLM to evaluate logical expressions at runtime.
LLMs are highly capable, but they are still probabilistic systems. That makes them unsuitable for decisions that need to be consistently correct.
For example, a critical condition such as credit_score > 75 should be evaluated by code. The code determines the correct branch, and that resolved branch is then passed to the LLM as an instruction. The LLM does the language work. The code does the logic work.
2. Do not allow LLMs to operate autonomous decisions inside their CRM systems.
An AI agent platform we reviewed, for example, allowed an LLM to log into a CRM and operate it like a browser agent. If a fuzzy customer-name search returns the wrong account, the LLM may proceed with the action anyway. At production volume, even a small probability of error can become a data incident.
A safer approach is to use the LLM offline to generate and test a deterministic browser automation script, validate the script properly, and then run it deterministically in production. The trade-off is that the script needs to be regenerated when the CRM interface changes. But because the LLM remains in the loop offline, that regeneration can happen quickly.
The principle is simple: use LLMs heavily offline and lightly online. In enterprise CX AI, this is what deterministic, system-level controls over prompt-layer controls looks like in practice.
4. Connect AgentOps to the rest of the business
Ownership and measurement solve one part of the problem. The other part is making sure AgentOps is not operating in isolation from the rest of the enterprise.
Customer issues are not static. A product launch creates a wave of new questions. A pricing or policy change creates a new category of edge case. A marketing campaign shifts demand overnight. None of these events change the agent's configuration directly, but they change what customers are asking it, and if AgentOps is only reviewing overall metrics like resolution rate, containment rate and CSAT, that shift stays invisible until it has already affected performance.
Two practices matter here.
1. Enterprises should establish an internal business update process.
Whenever there are business updates, AgentOps should be looped in early and take ownership of keeping the knowledge, workflows and playbooks the AI agent relies on up to date, before the change reaches customers rather than after.
2. The AI agent platform itself should give AgentOps the tools to capture gaps directly from real customer conversations.
This means that when the AI agent encounters a conversation it cannot solve, AgentOps can use the AI agent platform to identify and capture that moment from the conversation data itself. Those gaps then feed back into the knowledge base and workflow as structured input, turning real customer conversations into a continuous source of voice of customer intelligence.
Put together, this creates a customer intelligence flywheel: a business change shifts what customers ask about, AgentOps identifies the impact from real conversations, knowledge and workflows are updated, performance is measured against the new baseline, and what is learned feeds back into how the business plans its next change.
That compounding effect is also what raises the stakes. An AI agent amplifies whatever sits underneath it, good design or bad, so its performance ends up as a scorecard for how well the organisation understands that customer-facing changes land directly on support. Once that lands, upstream teams start feeding the flywheel proactively. Product loops CX in before a release. Marketing flags a campaign before it goes live. The question shifts from why CSAT dropped last week to what the next launch will generate and whether the team is ready.
Closing thought
Deploying an AI agent is a project. Running one well is a discipline. Enterprises that treat these four practices, intent design, AgentOps ownership, deterministic controls and organisational connection, as ongoing operating principles rather than a launch checklist are the ones whose agents keep improving instead of quietly drifting.
If you want to talk through how this applies to your own operation, get in touch with the Level3AI team.
