In a traditional WordPress site, one system stores the content and also builds the pages. A headless setup splits the two. WordPress stays as the place where editors write, while a separate front end, often built with a JavaScript framework, fetches the content through an API and displays it.

How it works

WordPress includes a REST API, and tools such as GraphQL plugins offer another way to request content. The front end sends a request, receives structured data and renders it. Editors keep a familiar dashboard, and developers get freedom over how the site looks and behaves.

Where it can help

Headless can make sense when the same content must appear in several places, such as a website and a mobile app. It also suits teams that already work with a JavaScript framework and want full control of the front end. Some teams choose it for performance, since pages can be generated ahead of time and served quickly.

What you give up

Many conveniences of a standard WordPress site no longer work out of the box. Themes, many page builders, previews, and plugins that output content on the front end may need to be rebuilt or replaced. Forms, search, SEO tags and menus all have to be handled in the new front end.

The extra running costs

You now maintain two systems: WordPress and the front end, plus the hosting and deployment that connects them. Updates, security and monitoring apply to both. For a small business site with a handful of pages, that overhead often outweighs the benefit.

Preview is a good example of the hidden work. In standard WordPress an editor clicks Preview and sees the page. In a headless setup the front end has to be wired to request draft content, which usually means authentication and a dedicated preview route. Teams that skip this step often leave editors publishing blind, and editors tend to complain about that quickly.

Caching and rebuilds need a plan too. If pages are generated ahead of time, an edited post has to trigger a rebuild or a cache refresh before visitors see the change. Webhooks can handle that, but someone has to set them up, watch them and fix them when they fail. A content team that expects changes to appear instantly should hear about this delay before the project starts.

A middle path is worth considering. Some sites keep a standard theme for most pages and use the API for one section only, such as a product catalogue that also feeds an app. That limits the new moving parts to the place where they pay off.

A simple way to decide

Ask three questions. Does the content need to reach more than one channel? Does the team have the skills to maintain a separate front end? Is there a clear problem that a standard setup cannot solve? If the answers are no, a well-built traditional site is usually quicker to launch and cheaper to run. For projects that do need a custom architecture, working with WordPress development experts early helps avoid costly rebuilds.

Whichever route you take, start with the content structure. Clear post types and fields make either approach easier to build.

Disclosure: I am affiliated with Chainbot Solutions, which is linked once above as a reference.

Headless WordPress: When It Makes Sense and When It Does Not was originally published in Let’s Code Future on Medium, where people are continuing the conversation by highlighting and responding to this story.

Step 1: Decide what the agent decides and what the workflow decides

AWS describes the split clearly: the agent decides when to run a workflow and what input to pass, and the workflow runs each step exactly as defined. A refund is a good example. The agent judges that a customer deserves one. The eligibility check, manager approval, and credit then happen in a fixed order every time. Write down which parts of your process need judgment and which are fixed rules. Fixed rules belong in code or workflows, never in a prompt.

Step 2: Expose what you already have

You do not need to rebuild processes to make them agent-ready. On AWS, existing Standard and Express Step Functions workflows can be targets for AgentCore Gateway, and the same idea works for any API you can describe. Start with a small set of well-understood operations, such as checking order status or starting a refund, and describe each one clearly: what it does, what inputs it takes, what it returns.

Step 3: Keep identity out of the model’s hands

One lesson from the MoEngage story is that tenant identity could never become a parameter the model gets to set. If the agent can choose which customer or account an action applies to, a confused or manipulated agent can act on the wrong one. Pass identity from the authenticated session, outside the model’s control, and make the tool reject anything else.

Step 4: Give each agent only the tools it needs

A support agent does not need the same tools as a reporting agent. Run separate tool servers with non-overlapping tool sets so each agent can only reach what its job requires. This limits the damage from mistakes and makes testing easier, since you can verify that a tool outside an agent’s set is refused.

Step 5: Let agents search for tools instead of loading them all

MoEngage found that listing all its tools would have cost more than 20,000 tokens per connection. Their agents instead search for tools by describing a task, and receive only the matches. If you expect dozens or hundreds of tools, add a search step rather than putting the whole catalog in context. It saves cost and helps the model pick correctly.

Step 6: Test the boundaries and log the calls

Write tests that call a tool the agent should not have and confirm it fails. Record every tool call with the agent, the inputs, and the result. When something goes wrong, you will want to see exactly which workflow ran and why.

The takeaway

Reliable agents are mostly about the plumbing around the model. Put fixed rules in workflows, expose them as narrow tools, keep identity out of the model’s reach, and let the model handle judgment. MoEngage estimates the managed approach saved about six months of platform engineering, which is a good reason to build on what you already have.

How to Turn Your Existing Workflows and APIs Into Reliable AI Agent Tools was originally published in Let’s Code Future on Medium, where people are continuing the conversation by highlighting and responding to this story.