From Automation to Accountability: Building AI Agents the Right Way

Building an AI agent—with the power to autonomously perform tasks or make decisions—has moved from sci-fi fantasy to real business practice. These intelligent systems can review data, generate insights, and automate workflows faster than humans. But with that capability comes risk: without a clear framework, AI agents can make errors, compromise privacy, or even damage a brand’s reputation. As organizations explore this technology, responsible design and implementation must be a core priority.

Martin Waxman—a communications strategist, adjunct professor at York University’s Schulich School of Business, and advisor at Ragan’s Center for AI Strategy—recently shared a practical framework for building AI agents in ways that are both effective and ethical. His advice, rooted in real use cases and professional experience, offers a roadmap for communicators and business leaders who are ready to experiment with agentic AI but want to avoid common pitfalls.

1. Start by Identifying the Right Tasks

One of the first steps in developing an AI agent is identifying tasks that are agent-ready—that is, tasks that are repetitive, structured, and predictable. AI performs best in environments where processes can be defined step by step. Daily or routine work such as monitoring social media sentiment, generating basic reports, sorting emails, or summarizing media signals are ideal candidates for automation. By focusing first on these well-defined tasks, teams can create agents that add value without introducing unnecessary risk.

2. Define Every Scope and Data Source Clearly

Before any coding begins, it’s critical to clarify exactly what the AI agent should do and what it should never attempt. This means defining the agent’s scope with laser focus—detailing the boundaries of its tasks and the data sources it may consult. Restricting access to trusted and relevant information prevents the AI from making “hallucinations,” or producing irrelevant or inaccurate outputs. For example, when Waxman built an assistant for his students, he instructed it to consult only the course syllabus and a set of pre-approved websites, narrowing its focus and keeping responses aligned with the intended purpose.

3. Break Down Workflows into Fine-Grained Steps

Humans naturally understand context and nuance, but AI does not. For this reason, every task must be mapped out into precise steps before automation begins. Documenting workflows with all decision points, exceptions, and dependencies ensures that the AI can replicate the work reliably. This also helps teams see where automation may introduce risk and where human supervision is non-negotiable.

4. Work Closely with IT from Day One

Deploying AI agents without engaging IT can lead to security vulnerabilities and integration problems. Rather than “shadow AI” solutions—tools used without organizational oversight—Smith stresses collaborating with technical teams in planning, implementation, and testing phases. IT’s involvement ensures secure deployment, compliance with internal policies, and alignment with broader tech infrastructure. Regular planning sessions with IT can also preempt many common integration challenges and safeguard systems from breaches.

5. Establish Guardrails and Governance Policies

Responsible AI use requires more than just smart engineering—it requires policy. Guardrails around ethical use, privacy, data safety, and approved tools make AI work strategic and reproducible. Cross-functional collaboration is key here: communications, legal, marketing, finance, and operations stakeholders all play a role in setting and enforcing these policies. A formal governance framework acts as a foundation for experimentation, helping teams innovate without jeopardizing trust or compliance.

6. Start Small and Pilot Before Scaling

Rather than launching AI agents across a company all at once, rollout should be incremental. Pilot programs let teams test on limited data sets and controlled environments, enabling them to catch errors, refine instructions, and measure impact before wider deployment. Controlled testing also gives teams time to tweak prompts and build confidence in the system’s outputs.

7. Always Include Human Oversight

Even the best AI systems are not perfect—and human oversight should never be optional. Teams should ensure that outputs from AI agents are reviewed by humans before any critical decision is made or action is taken. This guards quality, prevents errors, and adds an invaluable ethical checkpoint. In practice, this might look like a human analyst reviewing and interpreting AI-generated insights before they are shared or acted upon.

8. Continuously Revisit and Update

AI is not “set and forget.” Guardrails, prompts, and workflows should evolve with new data, changing business needs, and advancements in technology. Continuous evaluation ensures that agents remain effective, relevant, and safe. By scheduling regular reviews—just as organizations would with other dynamic systems—teams can adapt to new uses or address risks as they emerge.

In sum, the promise of agentic AI is immense—but so are the responsibilities that come with it. By identifying appropriate tasks, aligning with internal stakeholders, setting limits, and maintaining human supervision, organizations can harness AI agents to enhance productivity without sacrificing trust or safety. Leading with responsibility isn’t merely a best practice—it’s the foundation of sustainable innovation in the age of autonomous systems.