K
Katie Austin
Guest
Prompt engineering got us started. Agent design is what comes next.
Much of the conversation around AI has focused on writing better prompts and generating better outputs from large language models. While prompt engineering remains an important skill, organizations are now looking beyond individual AI interactions and asking how AI can become a trusted part of everyday workflows.
For content teams, that means moving beyond one-off tasks toward intelligent agents that review SEO, enforce brand guidelines, identify accessibility issues, maintain content quality and support editorial decision-making within the content management system.
Building effective AI agents isn’t about creating more of them. It’s about designing specialists with clearly defined responsibilities that work together within governed editorial workflows. Like any successful editorial team, each agent needs a clear purpose, appropriate permissions and well-defined boundaries.
Here, we’ll explore 10 best practices for building better AI agents in your CMS and explain how thoughtful agent design can help organizations create more consistent, trustworthy and scalable content operations.
The first step in designing an effective AI agent is understanding the editorial problem you’re trying to solve.
Too often, organizations ask, “What can AI do?” when the better question is, “What task do we want AI to help us with?” That shift in thinking changes everything. Instead of building technology in search of a purpose, you’re designing an agent to support a specific editorial responsibility.
Within a CMS, those responsibilities are often well defined. An agent might improve SEO metadata, review brand consistency, identify accessibility issues or flag outdated content. Each task has a clear objective, making it easier to measure success and easier for editors to trust the recommendations.
The goal isn’t to build one agent that does everything. It’s to build specialist agents with clearly defined responsibilities that work together to strengthen the editorial workflow.
Every agent should have clear limits on where it can operate and what it is allowed to do. Within a CMS, those boundaries might be defined by content type, individual fields, workflow stage or permitted actions.
Clear boundaries become even more important as organizations introduce multiple agents into the same workflow. An SEO agent and a Brand agent, for example, may both identify opportunities to improve a page title, but for different reasons.
Defining boundaries helps prevent agents from duplicating work, contradicting one another or making changes outside their intended role. Editors can see which agent is responsible for what, making the overall workflow easier to understand, govern and scale.
A single AI agent may be enough when the task has one clear objective. However, as content workflows become more sophisticated, it’s often better to divide responsibilities between multiple specialist agents.
An SEO agent optimizes for search visibility. A Brand agent focuses on tone and approved terminology. A Compliance agent checks regulated claims, while a Content Maintenance agent identifies outdated information or broken links. Although they may all review the same page, each has a different purpose and a different definition of success.
Specialization gives each agent a clear objective and a more focused definition of success. Rather than asking one agent to make every kind of content decision, organizations can combine several agents that each contribute a specific type of expertise.
The result is closer to a well-structured editorial team: different specialists review the same content through different lenses, while editors retain visibility into why each recommendation was made.
Once an agent’s role and boundaries are clear, it also needs explicit operating rules. It should know what information it can use, what a successful result looks like, how recommendations should be presented and when a decision should be handed back to a human editor.
These rules form the agent’s working contract. For example, an agent might be instructed to flag an issue rather than rewrite the content, explain the reason behind every recommendation or request human approval before suggesting changes to sensitive fields.
This becomes especially important within a CMS, where editors need clear, actionable guidance rather than open-ended commentary. Consistent outputs make it easier to compare recommendations, resolve conflicts and understand how an agent reached its conclusion.
A clear operating contract makes agent behavior more predictable and gives editors greater confidence in how AI participates in the broader content workflow.
For AI agents to work effectively together, they need a shared understanding of the organization they’re supporting. Brand guidelines, approved terminology, SEO best practices, target audiences and workflow automation and governance should all provide a common foundation for every agent.
Sharing context, however, doesn’t mean sharing authority.
Different agents have different responsibilities, and there will inevitably be times when their priorities don’t align. An SEO agent may recommend optimizing a headline for search visibility, while a Brand agent prioritizes tone and messaging. In these situations, organizations need clear rules that determine which recommendations take precedence and when human review is required.
Providing every agent with the same source of truth while clearly defining decision-making responsibilities creates a more consistent editorial experience and prevents agents from working against one another.
Adding more AI agents shouldn’t create more work for editors. Their role is to improve the content, not manage competing streams of feedback.
When multiple agents review the same page independently, the result can quickly become overwhelming. An editor might receive SEO recommendations, followed by brand suggestions, then another round of SEO updates triggered by the previous changes. Instead of streamlining the review process, AI can create unnecessary noise.
A better approach is to coordinate agent output before it’s presented to the editor. Related recommendations should be grouped together, duplicate suggestions removed and any conflicting advice clearly identified. This creates a more streamlined review process and allows editors to make informed decisions without constantly switching between competing recommendations.
Well-coordinated agents should feel like a collaborative editorial team, not a room full of people talking over one another.
AI agents shouldn’t treat every piece of content as if they’re seeing it for the first time. As editors accept and reject recommendations, those decisions provide valuable context that can improve future suggestions.
If an editor repeatedly dismisses the same recommendation, the agent should recognize that pattern and avoid making the same suggestion again. Remembering previous decisions reduces unnecessary repetition, improves relevance and creates a smoother editorial experience.
Ultimately, trust is built through collaboration. Editors will accept that AI won’t get every recommendation right, but they expect it to learn from their feedback. An agent that adapts over time becomes a far more valuable member of the editorial team than one that continually repeats the same advice.
A recommendation without an explanation is difficult to trust. Rather than simply suggesting changes, AI agents should explain why it’s needed, whether that’s improving keyword relevance, aligning with brand guidelines, reducing complexity or removing an unsupported claim.
Providing that context helps editors make informed decisions and builds confidence in the recommendations they’re reviewing. It also transforms AI from a black box into a collaborative editorial assistant that supports, rather than replaces, human judgment.
The more transparent an agent is about its reasoning, the more likely editors are to trust its recommendations and adopt them over time.
Not every AI agent should be given permission to make changes. Some are best suited to analyzing content and making recommendations, while others may be trusted to generate suggested edits or automate specific tasks within defined permissions.
The more autonomy an agent has, the greater the need for governance. Organizations should carefully align an agent’s capabilities with the level of risk involved, with appropriate permissions, workflow controls and auditability in place.
A simple rule of thumb is to let agents recommend broadly but act selectively. Human oversight remains essential whenever changes could affect compliance, brand integrity or the overall customer experience.
The true test of an AI agent is how it handles the complexity of real-world content rather than carefully prepared examples.
Every CMS contains legacy pages, inconsistent metadata, campaign content, regulated claims and years of accumulated edits. AI agents need to perform reliably across all of it, not just ideal scenarios.
Testing against real content helps identify gaps in an agent’s recommendations, improves reliability and builds confidence before it’s introduced into production workflows. The closer your testing reflects the reality of your CMS, the more effective your AI agents will be when it matters most.
The future of AI in content management is in designing a team of specialist agents that work together to support editors, strengthen governance and improve the quality of content decisions.
As AI becomes more deeply embedded within the CMS, success will depend on thoughtful design rather than technical capability alone. Agents with clear roles and coordinated workflows are more likely to earn the trust of content teams and deliver meaningful long-term value.
Those who embrace this approach will create content operations where AI and people work together to produce smarter, more consistent digital experiences.
Discover how Progress Sitefinity CMS can simplify content management, streamline workflows and help your team deliver digital experiences faster.
Book a Demo
Continue reading...
Much of the conversation around AI has focused on writing better prompts and generating better outputs from large language models. While prompt engineering remains an important skill, organizations are now looking beyond individual AI interactions and asking how AI can become a trusted part of everyday workflows.
For content teams, that means moving beyond one-off tasks toward intelligent agents that review SEO, enforce brand guidelines, identify accessibility issues, maintain content quality and support editorial decision-making within the content management system.
Building effective AI agents isn’t about creating more of them. It’s about designing specialists with clearly defined responsibilities that work together within governed editorial workflows. Like any successful editorial team, each agent needs a clear purpose, appropriate permissions and well-defined boundaries.
Here, we’ll explore 10 best practices for building better AI agents in your CMS and explain how thoughtful agent design can help organizations create more consistent, trustworthy and scalable content operations.
1. Start with the Editorial Job, Not the AI
The first step in designing an effective AI agent is understanding the editorial problem you’re trying to solve.
Too often, organizations ask, “What can AI do?” when the better question is, “What task do we want AI to help us with?” That shift in thinking changes everything. Instead of building technology in search of a purpose, you’re designing an agent to support a specific editorial responsibility.
Within a CMS, those responsibilities are often well defined. An agent might improve SEO metadata, review brand consistency, identify accessibility issues or flag outdated content. Each task has a clear objective, making it easier to measure success and easier for editors to trust the recommendations.
The goal isn’t to build one agent that does everything. It’s to build specialist agents with clearly defined responsibilities that work together to strengthen the editorial workflow.
2. Give Each Agent Clear Boundaries
Every agent should have clear limits on where it can operate and what it is allowed to do. Within a CMS, those boundaries might be defined by content type, individual fields, workflow stage or permitted actions.
Clear boundaries become even more important as organizations introduce multiple agents into the same workflow. An SEO agent and a Brand agent, for example, may both identify opportunities to improve a page title, but for different reasons.
Defining boundaries helps prevent agents from duplicating work, contradicting one another or making changes outside their intended role. Editors can see which agent is responsible for what, making the overall workflow easier to understand, govern and scale.
3. Build Specialist Agents, Not Generalists
A single AI agent may be enough when the task has one clear objective. However, as content workflows become more sophisticated, it’s often better to divide responsibilities between multiple specialist agents.
An SEO agent optimizes for search visibility. A Brand agent focuses on tone and approved terminology. A Compliance agent checks regulated claims, while a Content Maintenance agent identifies outdated information or broken links. Although they may all review the same page, each has a different purpose and a different definition of success.
Specialization gives each agent a clear objective and a more focused definition of success. Rather than asking one agent to make every kind of content decision, organizations can combine several agents that each contribute a specific type of expertise.
The result is closer to a well-structured editorial team: different specialists review the same content through different lenses, while editors retain visibility into why each recommendation was made.
4. Define How Each Agent Should Work
Once an agent’s role and boundaries are clear, it also needs explicit operating rules. It should know what information it can use, what a successful result looks like, how recommendations should be presented and when a decision should be handed back to a human editor.
These rules form the agent’s working contract. For example, an agent might be instructed to flag an issue rather than rewrite the content, explain the reason behind every recommendation or request human approval before suggesting changes to sensitive fields.
This becomes especially important within a CMS, where editors need clear, actionable guidance rather than open-ended commentary. Consistent outputs make it easier to compare recommendations, resolve conflicts and understand how an agent reached its conclusion.
A clear operating contract makes agent behavior more predictable and gives editors greater confidence in how AI participates in the broader content workflow.
5. Share Context, Not Control
For AI agents to work effectively together, they need a shared understanding of the organization they’re supporting. Brand guidelines, approved terminology, SEO best practices, target audiences and workflow automation and governance should all provide a common foundation for every agent.
Sharing context, however, doesn’t mean sharing authority.
Different agents have different responsibilities, and there will inevitably be times when their priorities don’t align. An SEO agent may recommend optimizing a headline for search visibility, while a Brand agent prioritizes tone and messaging. In these situations, organizations need clear rules that determine which recommendations take precedence and when human review is required.
Providing every agent with the same source of truth while clearly defining decision-making responsibilities creates a more consistent editorial experience and prevents agents from working against one another.
6. Coordinate Feedback Before It Reaches the Editor
Adding more AI agents shouldn’t create more work for editors. Their role is to improve the content, not manage competing streams of feedback.
When multiple agents review the same page independently, the result can quickly become overwhelming. An editor might receive SEO recommendations, followed by brand suggestions, then another round of SEO updates triggered by the previous changes. Instead of streamlining the review process, AI can create unnecessary noise.
A better approach is to coordinate agent output before it’s presented to the editor. Related recommendations should be grouped together, duplicate suggestions removed and any conflicting advice clearly identified. This creates a more streamlined review process and allows editors to make informed decisions without constantly switching between competing recommendations.
Well-coordinated agents should feel like a collaborative editorial team, not a room full of people talking over one another.
7. Learn from Editorial Feedback
AI agents shouldn’t treat every piece of content as if they’re seeing it for the first time. As editors accept and reject recommendations, those decisions provide valuable context that can improve future suggestions.
If an editor repeatedly dismisses the same recommendation, the agent should recognize that pattern and avoid making the same suggestion again. Remembering previous decisions reduces unnecessary repetition, improves relevance and creates a smoother editorial experience.
Ultimately, trust is built through collaboration. Editors will accept that AI won’t get every recommendation right, but they expect it to learn from their feedback. An agent that adapts over time becomes a far more valuable member of the editorial team than one that continually repeats the same advice.
8. Make Recommendations Explainable
A recommendation without an explanation is difficult to trust. Rather than simply suggesting changes, AI agents should explain why it’s needed, whether that’s improving keyword relevance, aligning with brand guidelines, reducing complexity or removing an unsupported claim.
Providing that context helps editors make informed decisions and builds confidence in the recommendations they’re reviewing. It also transforms AI from a black box into a collaborative editorial assistant that supports, rather than replaces, human judgment.
The more transparent an agent is about its reasoning, the more likely editors are to trust its recommendations and adopt them over time.
9. Match Agent Autonomy to Risk
Not every AI agent should be given permission to make changes. Some are best suited to analyzing content and making recommendations, while others may be trusted to generate suggested edits or automate specific tasks within defined permissions.
The more autonomy an agent has, the greater the need for governance. Organizations should carefully align an agent’s capabilities with the level of risk involved, with appropriate permissions, workflow controls and auditability in place.
A simple rule of thumb is to let agents recommend broadly but act selectively. Human oversight remains essential whenever changes could affect compliance, brand integrity or the overall customer experience.
10. Test Against Real Content, Not Perfect Examples
The true test of an AI agent is how it handles the complexity of real-world content rather than carefully prepared examples.
Every CMS contains legacy pages, inconsistent metadata, campaign content, regulated claims and years of accumulated edits. AI agents need to perform reliably across all of it, not just ideal scenarios.
Testing against real content helps identify gaps in an agent’s recommendations, improves reliability and builds confidence before it’s introduced into production workflows. The closer your testing reflects the reality of your CMS, the more effective your AI agents will be when it matters most.
From Prompts to Purpose
The future of AI in content management is in designing a team of specialist agents that work together to support editors, strengthen governance and improve the quality of content decisions.
As AI becomes more deeply embedded within the CMS, success will depend on thoughtful design rather than technical capability alone. Agents with clear roles and coordinated workflows are more likely to earn the trust of content teams and deliver meaningful long-term value.
Those who embrace this approach will create content operations where AI and people work together to produce smarter, more consistent digital experiences.
Discover how Progress Sitefinity CMS can simplify content management, streamline workflows and help your team deliver digital experiences faster.
Book a Demo
Continue reading...