A website assistant can answer questions. An AI agent may also use tools to retrieve information or carry out a task. That distinction matters: once a system can change records, send messages or trigger a workflow, a mistaken instruction can have consequences beyond an inaccurate answer.
Why agent security is receiving more attention
In its April 2026 paper on adversarial attacks against AI, the National Cyber Security Centre discusses the risks associated with agents that have access to systems and tools. It also describes indirect prompt injection, where hostile instructions can reach a model through content it retrieves.
For a customer service deployment, our practical recommendation is to design permissions and approval steps before expanding autonomy. A model’s instruction to “be careful” should not be the only thing protecting customer records. Source: NCSC, Understanding adversarial attacks against Machine Learning and AI, 29 April 2026.
Start with an approved service knowledge base
Define what the agent can answer: service descriptions, the proposal process, contact routes and other information the business has approved. Assign someone to update this material when services change. The assistant should acknowledge when it does not have an answer rather than inventing a price, a delivery date or a contractual commitment.
For example, an agent for a consultancy could explain the difference between a strategy engagement and a feasibility study, then direct the visitor to the appropriate proposal form. It need not access private client projects to perform that public role.
Separate answering from acting
Give each integration only the permissions it needs. A first release might retrieve approved public information without editing anything. A later release could prepare a draft action for a staff member to approve. Treat the authority to send, delete, approve or commit as a separate design decision.
Enforce these boundaries in the application and connected systems. Consider what a malicious visitor or a compromised document might ask the agent to do. Keep sensitive operations behind checks that do not depend solely on the model’s judgement.
Test the awkward conversations
Include ambiguous requests, outdated service questions, instructions to reveal private data and attempts to bypass the agent’s role. Check that the handover route works when the assistant cannot help. Measure useful answers and successful routing, alongside complaints, incorrect claims and failed interactions.
After launch, review behaviour when knowledge or integrations change. Keep a way to disable an unsafe capability promptly and give a named person responsibility for operational review.
Smart Flow AI’s AI Agent Creation service covers defined tasks, approved knowledge, integrations, testing and handover. Request a proposal to discuss an agent with a clear, useful scope.