An independent technical investigation series
AI agents are coming aboard. Has anybody checked the gangway?
Websites are being asked to do more than load quickly and look respectable. They increasingly need to expose reliable information to software that can retrieve it, interpret it and, in some circumstances, act on it.
Cloudflare and other technology providers are introducing tools for machine-readable content, content-use signals, API discovery, agent interfaces and access controls. Some may prove genuinely useful. Others may add complexity without solving a business problem. The trick is knowing which is which.
At Sydney Business Web, we'll investigate these technologies on our own working infrastructure before recommending them to clients. Each field note will explain the purpose, the configuration, what our tests actually showed, what remains uncertain and whether a business has a good reason to implement it.
Our working method
Examine. Analyse. Act — or don't. Write.
We're not here to switch on every beta feature or chase a perfect readiness score. We're here to establish what works, under what conditions, at what cost and with which risks.
Understand the claim
Read the documentation, identify the mechanism and define what a successful test would show.
Gather evidence
Measure actual HTTP responses, content, identity relationships, failures and side effects.
Act — or don't act
Deploy selectively, mitigate risks or leave a feature disabled when the case is unproven.
Publish the findings
Record observations, limits and commercial implications in a permanent reference.
The investigation index
Field notes
Each numbered investigation will become a permanent technical page. We will add links when the corresponding write-up is published, rather than pretending unfinished experiments have conclusions.
Cloudflare Markdown for Agents: What Actually Changes?
We enabled Cloudflare's Markdown conversion on one page only — the Sydney Business Web homepage — and requested it using Accept: text/markdown. We inspected response headers, converted content and our connected business-identity JSON-LD.
Verified evidence: HTTP 200; Content-Type: text/markdown; all six JSON-LD objects equal to their original parsed structures, including values and relationships. We have not established token savings, reliable agent uptake or improved AI recommendations.
Content Signals: Who may use your website's information?
Inspecting the policies communicated to AI systems, what their headers actually express and what they cannot enforce.
Agent Readiness Scores: Useful diagnosis or checkbox theatre?
Separating genuine business requirements from platform feature lists and readiness scores.
APIs, WebMCP and Agent Interfaces
When should an agent read a website, and when does it need a controlled interface to a business system?
Authentication, Permissions and Safe Agent Actions
How to distinguish useful automation from handing unfamiliar software the keys to the engine room.
What we investigate
Not just whether a feature switches on.
- Retrieval: Can the intended system actually obtain the information?
- Integrity: Are business facts, entity identifiers and relationships preserved?
- Interpretation: Is the content useful and unambiguous for the intended machine reader?
- Operation: What happens to caching, monitoring, performance and security?
- Commercial value: Does the capability solve a real problem worth its cost?
What we won't pretend
A successful request isn't a guaranteed recommendation.
A crawler successfully receiving a page is evidence of access — not proof that an AI model understands the business, trusts every claim or will recommend it to a customer.
We also won't imply that experimental standards are universally supported, that every business needs agent interfaces, or that turning on a platform feature automatically delivers a commercial benefit.
Engineering isn't the art of making everything complicated. It's the discipline of knowing what needs to work — and proving that it does.
The bigger picture
Readiness begins with a coherent business identity.
Machine-friendly formats and agent interfaces are only part of the story. A business website still needs accessible content, consistent identity, clear relationships between people and services, suitable controls and corroborating evidence. That's why these field notes sit alongside our work on Schema Gorilla and AI Observatory: one examines connected machine-readable identity; the other measures observable machine retrieval.
Those are different questions. Neither is answered by a colourful dashboard alone.
For related engineering groundwork, see our earlier Agentic Browsing Readiness investigation, AI Crawler Monitoring at the Cloudflare Edge, and our findings on why AI systems sometimes cannot retrieve websites, ModSecurity retrieval failures and HTTP 429 responses. Those are separate operational problems; Markdown conversion cannot fix blocked or inconsistent origin responses.