5 Biggest AI Agent Fails and How to Avoid Them

66% of companies implementing AI agents report higher productivity, and 57% say they cut costs. Sounds promising, right? Yet, Gartner predicts that by 2027, more than 40% of AI agent projects will be canceled.

The reason is simple: building a reliable AI agent is full of challenges. Too often, agents fail to complete tasks or only partially deliver, making adoption risky.

At Softcery, we’ve helped businesses build AI agents that get the job done. In this article, we’ll show you the most common pitfalls your tool might face, why they happen, and, most importantly, how to avoid them so your agent actually delivers in the real world.

Understanding AI Agent Fails: Top 5 Mistakes and Their Reasons

Let us start with the most common mistakes AI agents tend to make and clear explanations of the reasons it might happen. 

Hallucinations

AI agent hallucinations are cases where a digital assistant provides incorrect or misleading results. Basically, hallucinations are the results of a so-called “Trash in – trash out” scenario: if the system receives outdated, messy, or contradictory information, mistakes are almost guaranteed.

In business, such mistakes are expensive. A single error, especially in a public-facing or high-stakes scenario, can cost a company billions of dollars. In February 2023, Google posted a short demo on Twitter with its generative agent Bard answering a simple question: “What new discoveries from the James Webb Space Telescope can I tell my nine-year-old about?”Bard provided some interesting facts about space, but at the same time mistakenly claimed that the James Webb Space Telescope had taken “the first-ever images of a planet outside our solar system.”

The reaction was immediate. In addition to a flurry of negative comments under the post, Google’s stock dropped 7.7% the next day, wiping out roughly USD 100 billion in market value. 

Context Rot

During long or complex conversations, AI agents can forget earlier details. For example, a customer gives their account number to solve a banking issue, explains the problem, and later the agent asks for the account number again. That would be frustrating, right?

Modern AI can process a lot of information, but the real challenge is how that information is given. If key details are hidden among less important ones or shared in the wrong order, the agent may miss them. In other words, success isn’t about dumping more data in, but more about giving the right information, in the right order, and in the right format.

Integration Gaps

When an AI agent is not connected to the necessary business systems, users may get unstable behavior and unreliable information.

A real-world example comes from one of our integrations with an old XML-RPC system. On paper, the integration looked complete: the agent could fetch data, parse it, and respond. But in practice, some critical fields, like wholesale prices or delivery terms, never appeared in the responses. These fields were buried in separate tables and weren’t included in the integration structure.

The issue wasn’t that the integration “failed” technically. The agent was able to produce answers. The problem was that the team hadn’t fully tested or accounted for these hidden data points at the beginning. As a result, the AI gave responses that were incomplete or even misleading when users hit these edge cases.

Memory Loss

Memory is one of the most complicated parts of building production AI agents. It’s about what gets remembered, for how long, and under what safeguards. If your tech team gets this wrong, the system either forgets everything, which frustrates users or remembers the wrong things, which is simply dangerous.

For example, researchers from Macquarie University showed that in 76% of cases Meta’s BlenderBot could be tricked into storing false information in its long-term memory. Once “planted,” those fake memories showed up in later conversations as if they were facts. 

Over-Automation 

A common pitfall is businesses try to automate the hardest or riskiest processes first, instead of starting with simpler tasks. Another scenario is expecting an AI to run entire departments the way a full team would.

For example, fintech company Klarna recently implemented an AI agent support system. The company claimed that the integration does the work of 700 employees and therefore easily replaces a large portion of their workforce. However, after a short period, the company began hiring customer support employees again “due to declining service quality and customer dissatisfaction”.What went wrong? Even if agents powered by AI are perfect at handling repetitive requests, especially in low-risk areas like customer support, they can’t fully replace humans. Customers still expect to speak to a real person when their problem is unusual or complex.

How to Build an AI Agent That Actually Works: Softcery’s Best Practices 

Now, let’s see how you can minimise all of the above mentioned risks. 

1. Start Small, Watch Closely

When integrating AI with business systems, it’s not enough to just connect the obvious flows. Even if the agent can technically fetch and parse data, important details can be hidden or formatted in unexpected ways. Small misses, like missing fields for wholesale prices, can turn into failures once real users interact with the system.

The key is to start small and pay close attention. Explore every possible response from the system, understand how it works in detail, and test against real scenarios. Avoid rolling out to everyone at once. Instead, use a gradual approach:

• Let the agent generate responses, but don’t send them to users yet;
• Send responses in a “suggestion” format for a human expert to review and approve;
• Finally, allow the agent to send answers directly once confidence is high.

2. Build a Verified Knowledge Layer  

One of the first steps of building AI agents is making sure the data and integrations they rely on are well-structured. For data, this means cleaning it up, organising it, and clustering similar information together. For integrations, it involves reviewing APIs and fixing gaps so the agent can access everything it needs. Then, you need to make sure that AI only works with reliable information. That’s the reason Softcery relies on a two-layer fact-checking approach:

• Relevancy scoring: Every piece of data gets a relevance score and low-scoring information gets filtered out before the AI even sees it.
• Post validation: We also built an LLM-as-a-Judge system that checks each response for completeness and accuracy

3. Engineer Context 

One of the biggest challenges for AI agents isn’t just the total amount of data they can hold, but that too much context can cause them to make mistakes or hallucinate. 

You can prevent this failure by designing a smarter memory layer that will help the AI ​​store key facts and the agent can “remember” what is really important, such as customer preferences or case details.

Our team often uses a conversation summarisation buffer, which stores concise summaries instead of every individual message. On top of that, we build a user profile memory that grows over time as the person interacts with the agent. This profile isn’t a replacement for the conversation history, but an additional reference that keeps important context available for better reasoning.

4. Build Human Escape Hatches

Sometimes even the best AI agents can struggle to handle complex user queries, so human supervision is essential to avoid delays in the response process and maintain user trust.

In Softcery’s voice agent projects, we usually implement a fallback path: if a subscriber asks a human, the call is immediately forwarded. For chatbots, we have developed a “human in the loop” system so marked answers are forwarded to experts. These experts formulate the correct answer, which is then either added to the knowledge base or passed back to the chatbot.

To control the quality of an agent's answers, it is also necessary to monitor the indicators. Our team usually checks the share of answers confirmed by the LLM-as-a-Judge system or by real user feedback using likes and dislikes. Then, we display these indicators on specialised Retool dashboards, which give our clients a clear idea of ​​​​where their agents are succeeding and where they need to improve.

5. Design Integrations That Don't Fail

Poorly designed integrations are one of the most common causes of agent crashes and delays.

To prevent even the smallest workflow disruptions, we recommend:

• Using well-documented REST or GraphQL API;
• Keeping LLM away from sensitive integration logic (so that business rules don’t depend on AI);
• Adding fallback and retry mechanisms to handle inevitable errors.

From our experience, sometimes it makes sense to let the AI decide the next step, but in many cases, it’s safer to embed the logic into a predefined workflow. For example:

• If a user opens a ticket and we already have the Purchase Order ID, the workflow triggers the necessary API calls automatically at fixed points, we don’t leave that choice to the LLM.
• If the agent doesn’t have the data upfront (like missing account info), we let the AI ask the user first and then perform the API call once the info is collected.

This combination of predefined workflows and selective AI decision-making removes major points of failure and makes execution more predictable.

Conclusion

AI agents can bring tremendous benefits to businesses, but as the examples show, without the right design and safeguards, they may fail in critical ways: hallucinations, memory issues, and context loss.

The good news? These pitfalls are avoidable. At Softcery, we’ve seen firsthand how the right approach transforms AI from a risky experiment into a reliable business asset and we’ve already shared our best insights with you.

If your business needs expert guidance in implementing AI agents, get in touch with our team  or start by getting our free AI-to-Go Live Plan to check whether your agent is truly ready for deployment.