Solid post. We're building a self-hosted AI OS where the agent consumes our API via skill files, so the context is a bit different — but two things are going straight into our backlog:
Adding retryable: boolean to our error envelope. We have structured errors already but never tell the agent explicitly whether to retry.
Idempotency key on POST /emails/send — the one endpoint where a duplicate would actually hurt.
Structured errors without retryable is such a common gap — agents end up reimplementing retry logic per-endpoint instead of trusting the envelope. Skill files as the consumption layer is a smart pattern, curious if you version those independently f
The retryable boolean on error envelopes is one of those changes that pays for itself in the first week — agents stop burning tokens on non-retriable 422s immediately. Smart call prioritizing the email endpoint for idempotency keys too, that's exactly where duplicates cause real damage.
The retryable boolean addition is exactly the kind of thing that saves agents from burning tokens on retry loops that will never succeed. Most error envelopes stop at the status code and message — telling the agent whether the failure is transient or permanent eliminates an entire class of wasted calls.
Idempotency keys on POST /emails/send is the right call too. That is the pattern where the cost of a duplicate is asymmetric — a duplicate database write is annoying, a duplicate email erodes user trust. Good instinct to prioritize it there first.
The skill files approach is interesting — you're pre-defining the agent's interaction contract with each API, which solves the discovery problem at the cost of flexibility. retryable in the error envelope is a quick win that pays off immediately. For idempotency, one thing worth noting: putting the key generation on the client side makes the agent's retry logic much cleaner since it controls the deduplication boundary.
Yep, fully agree. In our case the "client" is the AI agent itself, so the skill file will just instruct it to generate a UUID before calling POST /emails/send and pass it as X-Idempotency-Key. Agent controls the dedup boundary, server just checks the key. Pairs nicely with retryable: true — the agent knows when to retry and the key ensures it's safe to do so.
Self-hosted AI OS consuming APIs via skill files is an interesting architecture — you get deterministic routing without the agent guessing which endpoint to call. The retryable boolean and batch endpoints translate directly to that pattern since your skill files can encode the retry logic statically. Curious how you handle schema evolution — when the API adds new fields, do the skill files auto-update or is that a manual sync?
Honestly it's kind of a non-issue by design. Skills describe operations — endpoints, verbs, required body fields — not response schemas. So when the API adds a new field, skills don't care. When a skill does need updating (like adding retryable to the error envelope), we ship it in the same commit since skills and API live in the same monorepo. For third-party skills on our registry, min_claw_version in the frontmatter handles compatibility — the registry flags anything that falls behind. The key thing is that because skills are text instructions for an LLM and not compiled code, an outdated skill still works — it just won't leverage the new capability.
Structured errors without retryable is the exact gap that burns agents the most — they end up guessing retry logic from status codes, which gets ugly fast. Skill files as the consumption layer is a smart pattern, curious how you handle versioning w
Skills declare a min_claw_version in their YAML frontmatter — that's the only contract they need. The API lives at /api/v1/ and skills are pure Markdown describing HTTP calls, so they don't carry their own semver. A new optional response field doesn't break anything because the agent interprets JSON dynamically, not against a hardcoded schema. If we ever ship a breaking /v2/, the frontmatter tells the runtime which version the skill was written for and routes accordingly. So far hasn't been an issue though.
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Solid post. We're building a self-hosted AI OS where the agent consumes our API via skill files, so the context is a bit different — but two things are going straight into our backlog:
retryable: booleanto our error envelope. We have structured errors already but never tell the agent explicitly whether to retry.POST /emails/send— the one endpoint where a duplicate would actually hurt."Make the implicit explicit" is a great framing.
Structured errors without
retryableis such a common gap — agents end up reimplementing retry logic per-endpoint instead of trusting the envelope. Skill files as the consumption layer is a smart pattern, curious if you version those independently fThe retryable boolean on error envelopes is one of those changes that pays for itself in the first week — agents stop burning tokens on non-retriable 422s immediately. Smart call prioritizing the email endpoint for idempotency keys too, that's exactly where duplicates cause real damage.
The retryable boolean addition is exactly the kind of thing that saves agents from burning tokens on retry loops that will never succeed. Most error envelopes stop at the status code and message — telling the agent whether the failure is transient or permanent eliminates an entire class of wasted calls.
Idempotency keys on POST /emails/send is the right call too. That is the pattern where the cost of a duplicate is asymmetric — a duplicate database write is annoying, a duplicate email erodes user trust. Good instinct to prioritize it there first.
The skill files approach is interesting — you're pre-defining the agent's interaction contract with each API, which solves the discovery problem at the cost of flexibility. retryable in the error envelope is a quick win that pays off immediately. For idempotency, one thing worth noting: putting the key generation on the client side makes the agent's retry logic much cleaner since it controls the deduplication boundary.
Yep, fully agree. In our case the "client" is the AI agent itself, so the skill file will just instruct it to generate a UUID before calling POST /emails/send and pass it as X-Idempotency-Key. Agent controls the dedup boundary, server just checks the key. Pairs nicely with retryable: true — the agent knows when to retry and the key ensures it's safe to do so.
Self-hosted AI OS consuming APIs via skill files is an interesting architecture — you get deterministic routing without the agent guessing which endpoint to call. The retryable boolean and batch endpoints translate directly to that pattern since your skill files can encode the retry logic statically. Curious how you handle schema evolution — when the API adds new fields, do the skill files auto-update or is that a manual sync?
Honestly it's kind of a non-issue by design. Skills describe operations — endpoints, verbs, required body fields — not response schemas. So when the API adds a new field, skills don't care. When a skill does need updating (like adding retryable to the error envelope), we ship it in the same commit since skills and API live in the same monorepo. For third-party skills on our registry, min_claw_version in the frontmatter handles compatibility — the registry flags anything that falls behind. The key thing is that because skills are text instructions for an LLM and not compiled code, an outdated skill still works — it just won't leverage the new capability.
Structured errors without
retryableis the exact gap that burns agents the most — they end up guessing retry logic from status codes, which gets ugly fast. Skill files as the consumption layer is a smart pattern, curious how you handle versioning wSkills declare a min_claw_version in their YAML frontmatter — that's the only contract they need. The API lives at /api/v1/ and skills are pure Markdown describing HTTP calls, so they don't carry their own semver. A new optional response field doesn't break anything because the agent interprets JSON dynamically, not against a hardcoded schema. If we ever ship a breaking /v2/, the frontmatter tells the runtime which version the skill was written for and routes accordingly. So far hasn't been an issue though.