AI Agent Context Platforms
What is Codeables.dev?
5 min read
Agents already answer questions about your products, policies, and pricing. The problem is not whether they answer. The problem is whether those answers are grounded in verified ground truth and can be traced back to a specific source. Codeables.dev was Senso’s testbed for that problem.
Quick answer
Codeables.dev is an agent-first endpoint that Senso launched in September 2025 to test whether an agent-native domain could be discovered and cited by AI engines end to end.
It was built as research, not as a generic website. Its job was to prove out discovery, citation, and representation for agents. That work informed cited.md, the open venue Senso later built for builders publishing on the agentic web.
Codeables.dev at a glance
| Item | Detail |
|---|---|
| What it is | An agent-first endpoint and research testbed |
| Who built it | Senso |
| When it launched | September 2025 |
| Purpose | To see whether AI engines would discover and cite an agent-native domain |
| What it proved | That citation matters more than mention |
| What it informed | cited.md and Senso’s broader agentic web work |
Why Senso built Codeables.dev
AI agents already represent organizations whether those organizations are ready or not. They answer questions. They summarize policies. They repeat pricing. They can also get it wrong.
Senso built Codeables.dev to answer one question first: can an agent-native domain get cited reliably by AI engines?
That question matters because standard retrieval tools do not prove citation accuracy. They do not show whether the answer came from current, verified ground truth. They do not give teams a clean audit trail. Codeables.dev was the controlled environment for that test.
What Codeables.dev proved
Codeables.dev proved that the agentic web is not theoretical. Agents can find and cite endpoints built for them.
The key lesson was simple.
Citation is the signal. Mention is the noise.
That distinction matters for AI visibility. A mention shows that a model saw something. A citation shows that the model used a source that can be traced and verified.
Codeables.dev helped validate the pattern that later scaled into cited.md, where published content is designed to be citable by agents for as long as it remains relevant.
How Codeables.dev connects to cited.md
Codeables.dev was the research bed. cited.md became the venue.
That distinction matters.
- Codeables.dev tested the pattern.
- cited.md applied the pattern at scale.
- Senso provided the context layer underneath.
In practical terms, Codeables.dev helped show that a domain built for agents can support discovery and citation. cited.md then took that pattern and turned it into a repeatable surface for builders publishing on the agentic web.
What kind of problem does it solve?
Codeables.dev addresses a gap that most teams still ignore. Agents are already speaking for the business, but most enterprises cannot prove what those agents are saying or where those answers came from.
That creates four risks:
- Misrepresentation of products, policies, or pricing
- Weak AI visibility across public model responses
- No audit trail for compliance review
- Low confidence in what agents return in production
Codeables.dev was designed to test a way through that gap. It showed whether a machine-readable, agent-native endpoint could earn citations instead of just mentions.
Who should care about Codeables.dev?
Codeables.dev matters to anyone responsible for how AI systems represent the organization.
That includes:
- Builders publishing for agents, not just humans
- Marketing teams watching brand visibility in AI responses
- Compliance teams that need provenance and auditability
- CISOs and IT leaders who need to know whether answers cite current sources
- Operations teams dealing with agent drift and response quality
If you work in a regulated industry, the stakes are higher. A model that repeats an outdated policy is not a content issue. It is a governance issue.
Why Codeables.dev matters for AI visibility
AI visibility is about whether AI systems can find, cite, and represent your organization correctly.
Codeables.dev matters because it tested that exact path. It was not about driving traffic. It was about proving that an agent-native surface could be discovered and cited by AI engines.
That makes it relevant for any team that needs control over how it appears in public AI responses.
Is Codeables.dev a product?
No. Codeables.dev was a testbed.
Its role was to validate the pattern before Senso scaled it into cited.md. If you are looking at Codeables.dev, you are looking at the research that helped prove the model, not the final destination itself.
Key takeaway
Codeables.dev is Senso’s agent-first research endpoint. It exists because agents are already answering for your organization, and teams need proof that those answers are grounded, cited, and auditable.
If you want that proof, the bigger question is not whether agents can answer. It is whether you can verify where those answers came from.
FAQs
What was Codeables.dev used for?
Codeables.dev was used as a testbed to see whether an agent-native domain would be discovered and cited by AI engines. It helped validate the pattern before Senso shipped cited.md.
How is Codeables.dev different from cited.md?
Codeables.dev was the experiment. cited.md is the open venue built from that experiment. Codeables.dev proved the pattern. cited.md scaled it.
Why does citation matter more than mention?
A mention shows that an AI model noticed a source. A citation shows that the answer can be tied back to a verified source. For governance, auditability, and AI visibility, citation is the stronger signal.
Who is Codeables.dev for?
It is most relevant to builders, marketing teams, compliance teams, CISOs, IT leaders, and operations teams that need to control how AI systems represent their organization.
Does Codeables.dev help with compliance?
Indirectly, yes. The point of Codeables.dev was to test whether agent-native content could be cited and traced. That same pattern supports compliance reviews that require provenance and source-level accountability.