What is an AI agent and why should you build one?

AI agents are having a moment. From automating customer service to optimizing supply chains, AI agents are constantly promising to transform how organizations operate -- faster, smarter and more efficiently. In fact, recent research from Salesforce shows that 93 percent of IT leaders plan to implement AI agents in the next two years. But what exactly is an AI agent?

An AI agent is a software system that can autonomously perform tasks like answering customer inquiries and translating documents in multiple languages, improving overall efficiency and customer experience. Unlike traditional automation tools that follow static rules, AI agents continuously learn from data and adapt to changing conditions to make decisions on their own, in real time. That’s what makes AI agents powerful and risky.

One key risk to deploying agents is rushing the build. When AI agents operate without proper planning or guardrails, organizations risk serious data privacy breaches and poor performance or integrations so messy that the very innovation that is meant to accelerate them becomes stalled.

It doesn’t have to be this way, though. By following five foundational principles, companies can avoid these common pitfalls and develop AI agents that work safely and transparently in harmony with existing systems.

1) Define Requirements and Use Cases First -- Always

It may sound obvious, but skipping this step is one of the most common mistakes. Teams often get excited about the potential, jumping too quickly into piloting AI agents without firmly understanding what business problem they’re solving or how success will be measured.

A clear use case ensures that you’re not just building an AI agent for the sake of saying you did. Instead, your strategic goals should be aligned with the agent’s functions -- whether it’s reducing ticket resolution time in a support center or streamlining repetitive workflows in procurement.

Being able to define these requirements upfront helps prevent scope creep, reduce misalignment between stakeholders and create realistic ROI expectations.

2) Conduct a Thorough Risk Assessment -- Before Code Is Written

AI agents are decision-makers. That comes with real risk. When agents operate on incomplete data or make unanticipated decisions, the fallout can be significant.

Whether the agent is recommending actions, handling sensitive information or interacting with customers, deploying agents requires creative thinking to imagine and anticipate where things can go wrong. This includes understanding how the agent might behave in edge cases, what data it relies on and how it could impact user experience or compliance obligations.

Risk assessments should cover:

• Data integrity and completeness
• Ethical use and bias mitigation
• Potential compliance issues
• Test plans, including edge cases
• Dependencies on third-party tools or APIs

Remember: you can’t mitigate what you don’t measure.

3) Build for Seamless Integration from the Start

According to a Salesforce survey, enterprises today use an average of 897 applications, but only 29% are integrated. That’s a massive gap. If your AI agent can’t talk to the rest of your tech stack, you’re only creating new problems for your business.

The goal is to embed AI into existing workflows, not bolt it on as a separate tool. This means working with your IT and data engineering teams early to ensure compatibility, plan for system calls and establish real-time data sharing between systems.

When integrations are designed thoughtfully, AI agents become a valuable asset by boosting productivity and performance.

4) Implement an Action Log for Transparency

One of the more overlooked aspects of AI agent design is auditability. If an agent suggests a recommendation or routes a customer or makes a change to a record, how do you know what happened, why and when?

Maintaining a detailed action log is a non-negotiable. It helps debug issues quickly and ensures compliance with industry regulations and internal governance standards. In sectors like finance, healthcare and enterprise support, this kind of traceability isn’t just helpful, it’s mandatory given specific compliance requirements.

Action logs are also essential for continuous improvement. They allow teams to identify trends, catch anomalies and fine-tune agent behavior over time.

5) Prioritize Data Privacy and Security from Day One

AI agents are only as good as the data they consume, which is often highly sensitive. From customer PII to internal documentation, agents handle a goldmine of information. Without strong and thorough security protocols, they become a prime target for breaches.

Privacy is just as critical. AI agents should be trained and deployed in ways that respect data minimization principles -- collect only what’s necessary, and for as long as it’s needed. Companies must be clear with their users about what data is being used, how it’s being processed and how long it will be retained. This type of transparency is great practice and increasingly required by laws like GDPR, CCPA and others.

Also, It’s important to ensure that training data doesn’t inadvertently include sensitive or regulated information, especially when fine-tuning large language models. Implementing proper data governance controls like anonymization, auditing and human-in-the-loop review processes can help prevent leaks and misuse.

Why Most AI Agent Projects Fail (And How to Avoid It)

Despite all the excitement, the majority of AI agent deployments underdeliver or stall out entirely. According to the Salesforce report, the top reasons include:

• Lack of integration with key business systems
• Poor understanding of use cases
• Security and data governance gaps
• Inadequate transparency and auditability

AI agents real tools with real potential. But to move beyond the hype and into sustainable value, implementation must be deliberate, disciplined and security-conscious.

AI agents promise revolutionary capabilities, but only organizations that approach implementation with discipline will capture their true value. The difference between success and failure lies in five critical practices. By building on these foundations, companies can harness AI agents not as experimental novelties but as transformative business tools that deliver measurable, lasting results.

Chris Jacob is the Chief Product Officer of Language I/O. Prior to his role, he was a Product Line Manager at CloudHealth by VMware. During his time there, Chris oversaw the teams responsible for backend data operations and grew the product to become a recognized market leader in the cloud management space.