Agentic AI systems go beyond traditional machine learning and chatbots. They are capable of intelligently automating real-world workplace processes end-to-end. These systems operate based on goals — not just instructions. They reason, decide, and act, which is why industries like retail are putting agents into the hands of their frontline retail assistants.

AI agents are making them more connected, giving them more visibility into inventory management and sales opportunities and requests, and intelligently automating tasks on the shop floor.

For example, an agent can interpret a customer’s return request and automatically trigger the associated logistics workflows. This includes initiating return approval, notifying the warehouse, and updating stock levels in near real time. They will be a digital autonomous worker augmenting the frontline worker.

Agents can also monitor inventory across locations and autonomously place supplier orders based on current trends. This kind of real-time optimization helps prevent overstocking or stockouts without manual intervention.

They can coordinate promotional activity by updating pricing across e-commerce, POS, and marketing systems. This ensures consistency and timeliness during time-sensitive campaigns. Some agents can even pull data from internal dashboards and external tools to create concise executive summaries — surfacing key insights for decision-makers.

Why Security is a Key Design Concern

Agentic AI unlocks massive value — but it also increases the system’s responsibility. These agents take steps that can directly affect operations, revenue, and customer experience. There are some real-world concerns that developers, IT and operational technology leaders need to be aware of and address:

• Prompt manipulation: malicious inputs — from customers or attackers — can cause agents to behave unpredictably, such as altering orders or issuing refunds.

• Tool misuse: an agent might access internal tools like pricing APIs or campaign systems not intended for autonomous control, creating unapproved changes.

• Oversight failures: without business logic context, an agent might repeat a failed task multiple times, unintentionally escalating an error, which could impact revenues or tarnish the companies Brand.

• Data leakage: AI-generated outputs could reveal confidential product performance, SKU-level details, or inventory patterns if not properly controlled.

• Automation drift: over time, agents may subtly shift behavior without detection, eventually misaligning with business goals or policies.

• Firewall / access control: clear rules on who and what can communicate with and through the Agent to prevent hijacking or misuse of the agent

These are not theoretical; they’re practical risks that increase with automation. The solution is not to avoid agentic AI — but to deploy it with secure, observable limits and governed design and collaboration with partners who can provide the agents, implementation, IT and developer support needed.

A Secure Lifecycle for Retail AI Agents

To deploy agentic AI responsibly, IT and OT leaders in industries like retail must embrace a lifecycle approach that balances innovation with control. This begins by defining clear agent boundaries by being explicit about what the agent is allowed to do autonomously, with a human-in-the-loop authorizing it — and equally clear about what it should never attempt.

Whether it’s initiating refunds, accessing customer data, or editing product listings, hard lines need to be drawn around the agent’s authority to prevent scope creep.

Next, consider threat model early in the design process using industry standards, and think like an adversary: how could someone trick the agent? Could it be misused internally or externally? Could it escalate its access? Mapping out abuse scenarios in advance helps identify controls before the agent ever sees production.

Look at hardening the prompts and internal logic the agent relies on. Avoid building agents that are overly general or capable of improvising beyond their business intent. Guardrails in how agents interpret instructions, reason through tasks, and make decisions are critical for safe autonomy.

And test collaboratively across teams before going live. Involve AI developers, operations, business stakeholders and security teams. This cross-functional testing helps uncover blind spots and ensures the agent performs safely across real-world use cases.

Finally, monitor and retrain agents post-launch. Behavior can drift over time — even in systems without direct learning loops. Set up real-time monitoring and observability pipelines, performance thresholds, and retraining checkpoints. Treat agents like evolving operational systems, not static deployments.

Five Principles for Secure Agentic AI in Retail

Agentic AI is a game-changer for industry — enabling faster decisions, connected frontline workers and intelligently automated operations. It’s an intelligent teammate that requires thoughtful onboarding, boundaries, and oversight.

1. Start with least privilege: give agents access only to the APIs and datasets they require — no more. Use access scopes, allowlists, and granular permissions. Avoid giving full control to agents unless business-critical and thoroughly vetted.

2. Harden the input surface: before feeding customer input into an agent, sanitize and validate it. Apply AI firewalls or prompt guards to prevent instruction injection. Clearly separate user inputs from system prompts to prevent confusion or abuse.

3. Observe more than outputs: log what the agent did and how it made its decisions. Track internal reasoning, external API calls, and unusual behavior like repeated refund attempts. Observation should go deeper than final outputs.

4. Isolate and control execution: run agents in sandboxed or staged environments for high-risk tasks. Restrict their internet access unless required. Use just-in-time credentials and frequently rotate tokens to minimize attack surfaces.

5. Test like an adversary: simulate edge cases, abuse inputs, and worst-case scenarios before production deployment. Ensure agents can’t access restricted systems like HR or finance. Treat agents like software — and red-team them regularly.

Finding the Right Partner

Developers, IT, and OT leaders in industries like retail who partner with AI providers and design agentic AI with security and governance from day one will lead — not just in innovation, but in trust and resilience.

Seek out AI partners who can provide industry trained ready-to-use AI agents to deliver faster return on investment, and the scope to develop and add more agents with the AI platform and tooling for creating, deploying, and maintaining agentic solution components across a product portfolio, enabling easy development of AI applications and solutions.

Prioritize partners with industry knowledge and a track record working closely with developers to discover what tools will be useful for a full end-to-end AI pipeline. This will support developers and software partners to collect data, train AI models, and deploy across customer devices with an AI software development kit (vision, voice, data, and GenAI) and pre-trained models.

And AI APIs for cloud, hybrid, and edge solutions will provide an easy-to-use ecosystem to integrate into any business application.

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Principal Cyber Security Architect, CTO Office, Zebra Technologies.