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Claude Architect · Lesson

Error Categories

Transient, validation, business and permission errors.

Error Categories is a free Claude Architect lesson on CoddyKit — lesson 2 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Claude Architect learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.

Why Error Categories Matter

When a tool fails inside an agent, how it reports the failure decides whether Claude can recover. A generic status like "Operation failed" tells the model nothing — it can't tell a temporary blip from a permanent block, so it either retries blindly or gives up.

The fix is a structured error: a small object that names the kind of failure. The core field is errorCategory, which takes one of four values:

  • transient — temporary, likely to succeed on retry
  • validation — the request was malformed
  • business — a domain rule blocked it
  • permission — access was denied

This lesson teaches you to recognize each category and route on it.

The Shape of a Structured Error

A well-designed MCP tool error carries more than a message. It flags itself as an error, names a category, and says whether retrying could help.

Notice the fields: isError marks it as a failure, errorCategory classifies it, and isRetryable gives an explicit yes/no on retrying. The extra fields — attempted_query and partial_results — let the coordinator recover intelligently instead of starting over.

# A structured tool error returned to Claude
{
  "isError": True,
  "errorCategory": "transient",   # transient | validation | business | permission
  "isRetryable": True,
  "message": "Upstream inventory service timed out after 5s",
  "attempted_query": "SELECT stock FROM inventory WHERE sku='A-19'",
  "partial_results": []
}

Transient Errors

Transient errors are temporary and not your request's fault: a timeout, a brief network drop, an upstream service that is momentarily overloaded. The same call, made again a moment later, is likely to succeed.

These map to isRetryable: true. The right move is almost always to retry locally inside the subagent — recover the fault where it happened, with a short backoff, rather than aborting the whole workflow over a hiccup.

{
  "isError": True,
  "errorCategory": "transient",
  "isRetryable": True,
  "message": "503 from payment gateway; service temporarily overloaded"
}

Validation Errors

Validation errors mean the request itself was malformed: a missing required field, a wrong type, an out-of-range value, a bad date format. The tool never got far enough to do real work.

Blindly retrying the identical request will fail again — so isRetryable is usually false. But the error is still recoverable: Claude can read the message, fix the input, and call again. The richer the message (which field, what was expected), the faster the model corrects itself.

{
  "isError": True,
  "errorCategory": "validation",
  "isRetryable": False,
  "message": "Field 'order_date' must be ISO-8601 (got '13/2026'); expected e.g. 2026-02-13"
}

Business Errors

Business errors happen when the request is well-formed and the caller is allowed, but a domain rule forbids the action. Think: refund exceeds the policy ceiling, account balance too low, item out of stock, booking past the cutoff time.

This is not a bug to retry — it's a real-world constraint. isRetryable is false. The agent should surface the rule clearly, and if the workflow can't proceed within policy, escalate rather than try to force the action.

{
  "isError": True,
  "errorCategory": "business",
  "isRetryable": False,
  "message": "Refund of $640 exceeds the $500 auto-approval limit; requires human approval"
}

Permission Errors

Permission errors mean access was denied: the credential, token, or role does not authorize this action or resource. The request may be perfectly valid in shape and intent — it's simply not allowed.

Retrying the same call with the same credential will keep failing, so isRetryable is false. The agent should not loop; it should report the access gap and, when appropriate, escalate to a human or a privileged path.

{
  "isError": True,
  "errorCategory": "permission",
  "isRetryable": False,
  "message": "API key lacks scope 'orders:write'; cannot process refund"
}

Routing on the Category

The whole point of categorizing is to route. Once the coordinator reads errorCategory, the recovery path is deterministic — it doesn't have to guess by reading prose.

A clean mapping:

  • transient → retry locally with backoff
  • validation → fix the input, then retry
  • business → respect the rule; escalate if blocked
  • permission → stop; report / escalate the access gap

This is intelligent routing that a generic "failed" status simply cannot support.

def route(err):
    cat = err["errorCategory"]
    if cat == "transient":
        return retry_with_backoff(err)      # recover in the subagent
    if cat == "validation":
        return repair_input_and_retry(err)  # not the same request twice
    if cat == "business":
        return escalate_if_blocked(err)     # respect the policy
    if cat == "permission":
        return report_access_gap(err)       # stop; do not loop

isRetryable Is the Fast Path

You don't always have to branch on all four categories. The isRetryable flag is a quick gate: true means "trying again could plausibly work," false means "the same call will fail the same way."

As a rule of thumb, only transient errors are retryable as-is. Validation needs the input changed first; business and permission won't change on retry at all. Treat isRetryable: false as a hard signal to stop retrying and choose a different action — repair, escalate, or report.

if err["isError"] and err["isRetryable"]:
    # transient: safe to try again with backoff
    result = retry_with_backoff(err)
else:
    # validation / business / permission: retrying won't help
    result = handle_non_retryable(err)

Access Failure vs Empty Result

A subtle but exam-critical distinction: an access failure is not the same as a valid empty result.

If a lookup tool can't reach the database, that's a transient error — maybe retry. If it reaches the database fine and there are simply no matching rows, that is a successful call returning zero results — isError is false. Conflating the two leads to pointless retries on "no matches found," or worse, silently treating a real failure as "nothing there."

# NOT an error — a valid empty result
{ "isError": False, "results": [], "message": "No orders match customer 8842" }

# An error — could not even run the query
{ "isError": True, "errorCategory": "transient",
  "isRetryable": True, "message": "Connection refused to orders DB" }

Carry Partial Results and Context

When a non-recoverable failure must propagate up, send structured context with it — not just "it broke." Include the failure type, the attempted_query, any partial_results already gathered, and viable alternatives.

In a multi-agent research system, this is what lets the coordinator annotate coverage gaps and still assemble a useful answer from what succeeded. The two failure modes to avoid: silent suppression (swallowing the error) and aborting the entire workflow because one of ten subtasks failed.

{
  "isError": True,
  "errorCategory": "permission",
  "isRetryable": False,
  "message": "No access to EU sales shard",
  "attempted_query": "sales WHERE region='EU' AND year=2026",
  "partial_results": [{"region": "US", "total": 4200000}]
}

Recover Local, Escalate Non-Recoverable

Put the two halves together into one operating principle for agent error handling:

  • Recover transient faults locally — retry inside the subagent with backoff; don't bubble a timeout all the way to the human.
  • Escalate non-recoverable failures with partial results — when business rules, permissions, or absent data block progress, hand up the structured context so a human or coordinator can decide.

Escalation triggers should be concrete (policy gaps, threshold violations, no progress after attempts, explicit human requests) — never based on sentiment or a model's self-rated confidence score.

Quick Check: Choosing the Recovery Path

Test your routing instinct on a realistic agent failure.

Recap: Four Categories, One Discipline

Structured errors turn a dead end into a decision. Remember the four categories and their default routes:

  • transient (isRetryable: true) → retry locally with backoff
  • validation → fix the input, then retry
  • business → respect the rule; escalate if blocked
  • permission → stop; report or escalate the access gap

Always distinguish an access failure from a valid empty result. When propagating a failure, carry attempted_query and partial_results — never suppress silently and never abort the whole workflow on one failure. Generic errors block recovery; structured errors with a category and isRetryable enable it.

Frequently asked questions

Is the “Error Categories” lesson free?

Yes — the full text of “Error Categories” is free to read here on the web, and the Claude Architect course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Claude Architect course, upgrade to CoddyKit PRO.

What will I learn in “Error Categories”?

Transient, validation, business and permission errors. You practise Claude Architect with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.

Do I need any experience to start Claude Architect?

No prior experience is required. Claude Architect on CoddyKit is structured for beginners through advanced learners; this is — lesson 2 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “Error Categories” lesson take?

Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.

Can I write and run code in this Claude Architect lesson?

Yes. Every Claude Architect lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.

All lessons in this course

  1. The isError Flag
  2. Error Categories
  3. Retryable Metadata & Partial Results
  4. Anti-Pattern: Generic Error Messages
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