From Hype to Commodity: 6 Critical Issues with AI Agents You Can’t Afford to Miss
Today, building a simple AI agent for corporate use looks like is a feasible task. Anyone familiar with Python can create a working agent in just a few hours using public LLMs such as OpenAI’s GPT. An AI agent built this way can quite effectively respond to questions from customers or employees on a given topic, using the business-specific information you’ve provided to the model. So, does that mean building AI agents for business is now easy, fast, and cheap?
Not quite!
There are many subtleties that are either invisible at first glance or seem easy to implement — but aren’t.
Let’s take a closer look at some of them.
1. Monitoring and Analyzing Agent Activity
Imagine an agent embedded on a company’s website or a mobile app, handling hundreds or even thousands of interactions daily with customers or employees.
So how do we monitor its performance? Suppose a customer is dissatisfied with the agent’s responses and calls the contact center or their account manager. How does that person review the conversation? How do they find out what questions were asked and what answers were given?
Clearly, all communications involving AI agents must be stored in a database, and a special user interface is needed to quickly search for and review an agent’s interactions with a specific customer.
Storing and analyzing these conversations is also essential for improving the quality of agent performance — regularly identifying questions the agent couldn’t answer or situations where it was ineffective. Based on this data, the agent’s knowledge base and instructions should be updated continuously.
To do this efficiently, you need specialized conversation analytics tools capable of processing and analyzing thousands of dialogues.
What should these tools look like? Obviously, manual review by company staff is expensive and inefficient. A better solution is an AI analyst agent — a specialized AI agent that reads through conversations, analyzes them using an LLM, and generates recommendations for improving agents, their knowledge, and their logic.
This AI analyst agent should be easy to create, connect to each AI agent used by the organization, and have a convenient user interface.
2. Human Handoff (AI-Human Collaboration)
What if during a conversation the user requests to be transferred to a human operator — because they’re not satisfied or simply prefer human interaction?
How do we enable that?
We need a specialized operator console where a human can join the conversation, view a summary of the discussion, and if needed — the full dialogue history.
This console should include tools for task distribution among human operators and mechanisms to notify them when intervention is required.
Ideally, it should also be equipped with an AI co-pilot to assist the human in responding more quickly and effectively after joining the conversation, along with tools for evaluating operator performance.
3. Multiple AI Agents
In any modern organization, multiple AI agents are already in use — or soon will be.
Imagine a client simultaneously interacting with several AI agents:
Asking the in-app AI agent for details about recent transactions or current rates;
Clarifying questions with an outbound AI email or AI WhatsApp sales agent that proactively reached out with a cross-sell offer;
Answering a phone call from an AI collector reminding him about an upcoming loan payment.
As we can see, this situation demands parallel communication with multiple AI agents across various topics and possibly across multiple channels.
For instance, a widget inside a mobile app must support concurrent sessions with both inbound and outbound AI agents — letting the user return to any ongoing topic at any time.
Just as important: company employees must have a 360-degree view of all ongoing and past interactions with that customer — regardless of how many different AI or human agents were involved or how many active dialogues are in progress.
Furthermore, all AI agents should ideally be aware of the customer’s full context — and capable of handing off a conversation to another AI agent when a query falls outside their scope.
4. Private Deployment
For some organizations, such as financial institutions, sharing customer or financial data in public clouds may be unacceptable.
In these cases, the entire solution — including the LLM and components like communication history databases — must be deployed on-premises or in a private cloud.
This deployment must still guarantee the necessary reliability, availability, and scalability of the agents and the tools used to monitor and manage them.
The implementation of scalability and fault-tolerance must be separated from the agent’s business logic. Otherwise, you’d need to reimplement these aspects every time a new AI agent is built.
And unlike cloud-based solutions, private deployments require you to also implement authentication, authorization, auditing, and monitoring mechanisms.
All this demands appropriate architectural patterns — and that, in turn, requires skilled architects, time, and resources.
5. Integration with Channels and Applications
What channels should AI agents support? Web widgets? Mobile apps? Messengers (WhatsApp, Telegram, Facebook Messenger, etc.)? Voice (SIP, Twilio)? Email?
The answer is: all of them — or at least most. That means significant integration work.
For example, every email service and client (Gmail, MS Outlook, Apple iCloud Mail, etc.) has its own quirks that need to be accounted for.
The same goes for messengers — each has its unique specifics that must be separately supported.
And when it comes to backend integration, it’s obvious that an effective AI agent must be able to both read and write to internal systems in real time via APIs. This is essential for answering customer questions, carrying out transactions, logging incidents, or scheduling meetings.
In other words, we need to be able to quickly and easily integrate AI agents with any internal or external systems — and keep those integrations up to date when APIs change.
6. Continuous Availability
We’re used to ChatGPT being available 24/7 — and assume that any AI agent should behave the same. But that’s not always the case.
And I’m not just talking about technical uptime.
Suppose an AI email agent sends a message to a customer, and the customer replies a few days later. We expect the agent to read and respond to that message appropriately.
Or imagine a voice conversation with an AI agent is interrupted and resumed the next day.
What if, in the meantime, the agent’s logic has changed — or the system was taken offline for maintenance?
We need to ensure that every conversation can continue from exactly where it left off, regardless of such interruptions.
How? The “only” thing required is to persist all communications in real time in a durable database — and to resume dialogues from the correct point.
Conclusion
There are many more nuances that every business will inevitably encounter when deploying AI agents at scale — far too many to cover in this short article.
In conclusion, I’d like to emphasize that AI agents are not just about their creation. Building one is only the beginning. The real challenge lies in managing the entire lifecycle: monitoring, scaling, integration, security, and continuous improvement.
For companies looking to adopt AI agents at scale, it’s not about building one-off prototypes — it’s about creating a system where agents are a reliable, secure, and fully integrated part of the business infrastructure.
That’s exactly why we’re building Flametree — a platform designed to solve these foundational challenges, so our clients don’t have to start from scratch every time. With Flametree, AI agents become part of a long-term strategy, not just a short-term experiment.