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Making a SaaS installable by an AI agent is not the same as exposing an API. The agent must be able to discover the product, obtain credentials, identify capabilities, install the right client, interpret errors, complete every consequential action, and verify the result without a dashboard handoff. While adapting the waitlist SaaS Waitlister, Devin D found failures in all of those seams: a proxy returned 403 for agent documentation, a large documentation file timed out, guessed package names failed, valid test emails were rejected, fake 404s misled agents, and the API stopped before the landing page was published. The fix was a complete agent surface: full API coverage, an agent-oriented skill.md, a valid OpenAPI document, package aliases, typed MCP tools, compact documentation indexes, and tests through real fetchers.
How do I make my SaaS installable by AI agents?
Start by treating the entire customer job as an executable contract. An agent needs a deterministic path from “I want this outcome” to “the outcome is verified,” with no step that silently requires a human to open your dashboard.
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- Complete API coverage: every operation required for the waitlist workflow, including creating, updating, publishing, unpublishing and deleting the associated landing page.
- An agent-facing
skill.mdroute: a decision tree, both supported credential paths and a self-check that lets an agent confirm its setup before changing data. - A fixed OpenAPI document:
/openapi.json, valid OpenAPI 3.0.3, documenting five endpoints, authentication, rate limits and error shapes. - A real npm SDK: plus aliases for likely guessed package names:
waitlister-js,waitlister-sdk,waitlister-nodeand@waitlister/sdk. Each alias re-exports the canonical package and carries a deprecation notice. - An MCP server: exposing 14 typed tools so a tool-using agent can discover operations and their input schemas.
- Machine-readable documentation indexes:
llms.txtandllms-full.txt, with routing and labeling that survive summarization.
The key design test is simple: remove dashboard access and run the job from a new account. Any step that cannot be completed from the published agent surfaces is still a human dependency.
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Why partial automation fails
A waitlist can be created successfully while the customer’s actual goal remains unfinished. If the landing page cannot be generated and published through the same agent-accessible interface, the process ends in a handoff. That is not an edge case; it is a broken workflow boundary. Define the desired outcome first, then map every write, read, publication and cleanup action needed to reach and verify it.
What broke when the agent surfaces were tested?
The successful run exposed assumptions that ordinary browser testing had hidden. Each failure occurred between the response emitted by the server and the response an agent toolchain actually received.
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A proxy turned documentation into a 403
The agent-readable llms.txt returned HTTP 403 because a vendor proxy forwarded bot user agents to a vendor whose AI-bot blocking was enabled. A direct request from the author’s side did not represent the path used by the agent. Access policy, proxy behavior and user-agent handling therefore belong in integration tests, not just server tests.
A file that was fast to serve still timed out
llms-full.txt was 961 KB. It took about 1.3 seconds to serve from the author’s side, yet an answer-time fetcher timed out after 60 seconds. A file can be healthy at the origin and unusable in the retrieval pipeline. Keep the full index available, but provide compact, directly useful routes that do not force an answer-time system to ingest a large document.
Package-name guesses became installation failures
Agents guessed package names instead of selecting the canonical one. The four aliases turned those guesses into successful installation paths while preserving a deprecation notice. This is a low-cost compatibility layer: reserve plausible names, make each package re-export the same SDK, and tell future maintainers which name is canonical.
Valid disposable test addresses were rejected
Email validation rejected plus-tag addresses, even though agents use them for disposable tests. Automated clients often generate syntactically valid variants to isolate test runs. Validation should reject malformed or unsafe input, not a legitimate addressing convention merely because a human signup form rarely uses it.
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Soft 200 responses made nonexistent pages look real
Unknown documentation slugs returned a successful-looking 200 response instead of a real 404. An agent could then trust a page that did not exist and continue with invented instructions. Status codes are part of the semantic interface: return 404 for an unknown route, and make the response body explain what the agent can try next.
Labels caused the right guide to be ignored
The documentation was split across pages. A single-page guide existed, but its label made a summarizer treat it like a sidebar. Retrieval systems do not always preserve a site’s visual hierarchy. Put the canonical guide under an unambiguous title, link it from the index, and avoid labels that imply optional or secondary content.
How should I publish an agent-readable skill.md?
Write skill.md as an executable decision tree, not as marketing documentation. An agent should be able to choose a credential path, call a self-check, select the next capability and recover from common errors without guessing.
Include both credential paths
Document both credential methods your product supports, with the exact location where each credential is supplied and the permission it grants. Do not make the agent infer whether a token belongs in a header, an environment variable or an SDK constructor. If one path is preferred, say so while keeping the alternative explicit.
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Give the agent a harmless verification operation before any create or publish action. The self-check should confirm that authentication works, the account is reachable and the response shape is understood. A failed self-check should identify the next branch in the decision tree rather than returning a generic error.
Describe the workflow in dependency order
- Authenticate through one of the documented credential paths.
- Run the self-check and confirm the expected response.
- Create the waitlist resource.
- Create or update its landing page.
- Publish the landing page.
- Submit a test address and verify the public signup without authentication.
- Unpublish and delete test resources when the task is complete.
The order matters. Publishing before the page exists, or testing a public signup before publication, creates failures that look like permission problems but are actually sequencing errors.
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Keep retrieval predictable
Link the decision tree, credential instructions, capability list, error guidance and self-check from a stable route. Use short sections with descriptive headings. A large catch-all file can remain available for bulk retrieval, but the critical path should be reachable through small, direct documents.
Should my SaaS expose OpenAPI or MCP?
Expose both when you can: OpenAPI gives agents and client generators a standard HTTP contract, while MCP supplies typed, discoverable tools. Neither replaces an end-to-end workflow or clear documentation.
| Surface | What it contributes | Where it can fail | Role in an agent-ready product |
|---|---|---|---|
| API | Direct execution of reads and writes | Partial coverage leaves a human handoff | Foundation for the complete job |
| CLI | Repeatable command-line interaction when a user or agent can run a process | Commands and flags still require discovery and consistent exit behavior | Optional convenience layer, not a substitute for the API contract |
| MCP | 14 typed tools in the Waitlister implementation for capability discovery and structured calls | Tool schemas cannot compensate for missing backend operations | High-level tool interface over the same complete capabilities |
| Documentation indexes | Machine-oriented discovery through llms.txt and llms-full.txt |
Proxy blocking, retrieval size, timeout and summarization can distort what is seen | Explain authentication, sequencing, limits and recovery |
The fixed /openapi.json route is especially important. The Waitlister specification was valid OpenAPI 3.0.3 and covered five endpoints, authentication, rate limits and error shapes. A stable URL lets a client fetch the contract without guessing a versioned path; the version inside the document then gives you a controlled place to evolve it.
Why do agents need real 404s?
Agents use status codes to decide whether to retry, change a path, request credentials or abandon a route. A soft 200 erases that signal. When an unknown documentation slug returns a normal-looking page, the agent may summarize nonexistent instructions and then switch tools or invent parameters.
Return a genuine 404 for an unknown resource or documentation route. Pair it with a concise machine-readable error shape and, where useful, a link or list of valid next routes. Use 401 or 403 for authentication and authorization failures, rate-limit responses for throttling, and validation errors for malformed input. The OpenAPI description should document these shapes so generated clients and MCP wrappers can handle them consistently.
How do package aliases and input validation affect installation?
Installation starts before the first API call. Agents infer package names from natural language, examples and ecosystem conventions. If only one exact name works, a harmless guess becomes a hard stop.
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Publish aliases that install the same SDK and clearly mark the canonical package. In this case, waitlister-js, waitlister-sdk, waitlister-node and @waitlister/sdk redirect to the real package and include deprecation notices. Keep the aliases synchronized with the canonical release so a guessed name does not produce an older or subtly different client.
Apply the same tolerance to test data. Plus-tag email addresses are valid and useful for isolating automated runs. Accept them unless there is a specific security or deliverability reason not to, and explain any rejected format in a structured validation response. Agents can recover from a clear validation error; they cannot reason reliably about an undocumented rejection.
How do I test what an AI fetcher actually receives?
Use the fetcher, proxy and retrieval path your users’ agents use. Devin D’s rule is exact: “Test with the real fetchers, not with curl.” The companion warning is just as important: “Curl told me everything was fine. It was, for curl.”
A practical test matrix should include:
- Direct origin requests and requests through the production proxy.
- Normal browser user agents and the bot user agents used by answer-time systems.
- Fresh retrieval of
skill.md,llms.txt,llms-full.txtand the OpenAPI route. - Unknown documentation slugs to verify real 404 behavior.
- Small and large responses, with a timeout budget that matches the consuming tool.
- Summarized output, checking that the canonical guide remains labeled as primary documentation.
Record status code, response size, elapsed time, redirects, cache behavior and the final text visible to the agent. The 961 KB full index, its approximately 1.3-second origin response and the separate 60-second answer-time timeout demonstrate why all three measurements—origin, transport and consumer—matter.
What did the complete agent run prove?
The final live agent completed the entire job without anyone touching the dashboard:
- It created a waitlist in an empty account.
- It generated and published the landing page.
- It submitted a test address.
- It verified the public signup without authentication.
- It unpublished and deleted the test work.
The reported timings show the practical effect of removing guesswork: a complete integration plan took about 90 seconds from public surfaces, and creating a waitlist from an empty account took three minutes 18 seconds. Across four consecutive live runs, the agent made zero wrong guesses. Those figures belong to Devin D’s 2026 runs, not a universal performance promise, but they show what deterministic surfaces make measurable.
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What should I fix first when making a SaaS agent-ready?
Prioritize failures that strand the user or cause the agent to believe a false result.
- Map the complete job: list every create, update, publish, verify and cleanup action, then expose each one through the API.
- Stabilize discovery: publish
skill.md,/openapi.json,llms.txtandllms-full.txtat predictable routes. - Make contracts machine-readable: document credentials, rate limits, request schemas, success responses and error shapes.
- Harden routing: return real status codes, especially 404, and test unknown paths.
- Increase tolerance safely: add package aliases and accept valid automated-test inputs such as plus-tag addresses.
- Test from the agent’s vantage point: include proxies, user agents, summarizers, response limits and timeout behavior.
- Run destructive cleanup deliberately: verify that unpublish and delete operations are available and clearly separated from ordinary creation.
Recheck these surfaces whenever the product, package registry, proxy configuration or documentation structure changes. The article and the Waitlister surfaces describe one 2026 implementation; routes, aliases and metrics can change over time.
The original account of this work is Devin D’s post on DEV Community.
Frequently Asked Questions
Can an AI agent install a SaaS without an SDK?
Yes, if the HTTP contract, authentication instructions, schemas and error behavior are complete and easy to retrieve. An SDK reduces implementation work, but it cannot repair a missing operation or an ambiguous workflow.
How often should agent-facing documentation be tested?
Test it whenever you change routing, authentication, package metadata, proxy rules or documentation labels. Add a scheduled fetch through the same consumer path as your users so a new 403, timeout or soft 200 is detected before an agent encounters it.
What should a deprecation notice on an SDK alias say?
Name the canonical package, explain that the alias remains a compatibility path, and point maintainers toward the replacement. Keep the alias installable long enough to absorb existing agent-generated projects rather than removing it immediately.
How can I tell whether an agent completed the job rather than merely calling tools?
Require an observable postcondition, such as an unauthenticated request to the newly published signup page, and record the resource identifiers returned by each step. Cleanup should then confirm that the created resources are no longer published or present.
Quick Recap
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