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docs(styleguide): add canonical reference for Data-Oriented Error Handling

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# Data-Oriented Error Handling
> **Status:** Active convention as of 2026-06-11. Established by the
> `data_oriented_error_handling_20260606` track. Canonical reference for all
> Python error-handling decisions in this codebase.
This styleguide codifies Ryan Fleury's "errors are just cases" framework as the
project convention. The 5 patterns below replace `Optional[T]` returns and
exception-based control flow with `Result[T]` dataclasses and nil-sentinel
dataclasses. SDK-boundary exceptions are caught and converted to `ErrorInfo`;
the rest of the application works with data, not control flow.
Reference: [Ryan Fleury, "The Easiest Way To Handle Errors Is To Not Have
Them"](https://www.dgtlgrove.com/p/the-easiest-way-to-handle-errors).
Independent corroboration: Timothy Lottes (`ERROR[__line__]: _code_` exit
pattern; each error code has exactly one meaning — never overload `UNKNOWN`),
Valigo ("Exceptions are horrifying"; modern languages without legacy baggage
move away from exceptions — Rust, Jai, Zig, Odin).
---
## The 5 Patterns
### 1. Nil-Sentinel Dataclasses (replaces `None`)
When a function would "return None" in conventional Python, return a
nil-sentinel dataclass instead. The sentinel has all default values
(zero-initialized) and is safe to read from.
```python
from dataclasses import dataclass, field
@dataclass(frozen=True)
class NilPath:
exists: bool = False
read_text: str = ""
errors: list[ErrorInfo] = field(default_factory=list)
NIL_PATH = NilPath() # module-level singleton
```
Callers don't need `if x is None:` checks; they can call `x.read_text` and
get `""` on the nil path.
**Convention:** `NIL_*` (uppercase) is the module-level singleton. `Nil*`
(PascalCase) is the class. Frozen dataclass prevents runtime mutation.
### 2. Zero-Initialization (via `@dataclass` defaults)
Fresh memory from the OS is zero-initialized. In Python, `@dataclass` with
field defaults achieves the same: the data is in a valid "empty" state
without any explicit constructor logic.
```python
@dataclass(frozen=True)
class String8:
text: str = ""
size: int = 0
```
Code that consumes `String8` (e.g., a for-loop bounded by `size`) works
correctly with the zero-initialized instance.
**Convention:** Mutable defaults use `field(default_factory=list)` (NOT `= []`,
which is shared across instances).
### 3. Fail Early (push validation to shallow stack frames)
Don't defer error checks to deep in the call stack. Push them to the entry
point so the user knows ASAP if the operation cannot succeed.
```python
def do_thing(path: Path) -> Result[str]:
resolved = _resolve_path(path) # validation happens HERE, not deeper
if not resolved.ok:
return Result(data="", errors=resolved.errors)
...
```
**Convention:** `assert` at entry points for invariants. Early `return` for
user-facing errors. `try/finally` (Python's analog to `goto defer`) for
cleanup.
### 4. AND over OR (Result with side-channel errors; no sum types)
Instead of `Union[T, E]` or `Result<T, E>`, return a struct with BOTH data
and errors as parallel fields:
```python
@dataclass(frozen=True)
class Result(Generic[T]):
data: T # the happy-path result (zero-initialized on failure)
errors: list[ErrorInfo] = field(default_factory=list) # side-channel; empty = success
```
Callers:
```python
r = do_thing(path)
if r.errors:
for err in r.errors: log(err.ui_message())
# use r.data regardless (it's the zero-initialized value on failure)
```
**Convention:** `Result` is generic over `T` (the success data) but NOT over
the error type. Errors are always `list[ErrorInfo]` (a side-channel list, not
a tagged sum). This collapses the bifurcated `if r.ok: ... else: ...`
codepaths into a single flat codepath.
### 5. Error Info as Side-Channel (not as exception)
Errors flow as DATA in the `Result` struct, not as exceptions. SDK
boundaries (which must catch vendor exceptions) convert them to `ErrorInfo`:
```python
@dataclass(frozen=True)
class ErrorInfo:
kind: ErrorKind
message: str
source: str = ""
original: BaseException | None = None
def ui_message(self) -> str:
src = f"[{self.source}] " if self.source else ""
return f"{src}{self.kind.value}: {self.message}"
```
**Convention:** `ErrorInfo` is the canonical error type. The legacy
`ai_client.ProviderError` exception class is removed; SDK helpers
(`_classify_<vendor>_error()`) RETURN `ErrorInfo` instead of raising.
---
## The Data Model
The canonical types live in `src/result_types.py`:
| Type | Form | Purpose |
|---|---|---|
| `ErrorKind` | `str, Enum` (12+ values) | Canonical error taxonomy: `NETWORK`, `AUTH`, `QUOTA`, `RATE_LIMIT`, `BALANCE`, `PERMISSION`, `NOT_FOUND`, `INVALID_INPUT`, `NOT_READY`, `UNKNOWN`, `CONFIG`, `INTERNAL`, plus optional `PROVIDER_HISTORY_DIVERGED_FROM_UI` for app-vs-provider-state-divergence cases. Each value has exactly one meaning. |
| `ErrorInfo` | `@dataclass(frozen=True)` | A single error: `kind: ErrorKind`, `message: str`, `source: str = ""`, `original: BaseException \| None = None`. Frozen; carries `ui_message()` for display. |
| `Result[T]` | `@dataclass(frozen=True)` `Generic[T]` | The success-or-failure container: `data: T`, `errors: list[ErrorInfo] = field(default_factory=list)`, `ok: bool` property, `with_error()`, `with_errors()`, `with_data()` methods. |
| `NilPath` | `@dataclass(frozen=True)` + `NIL_PATH` | Nil-sentinel for filesystem paths. Has `exists=False`, `read_text=""`, `errors=[]`. |
| `NilRAGState` | `@dataclass(frozen=True)` + `NIL_RAG_STATE` | Nil-sentinel for the RAG engine. Has `enabled=False`, `is_empty_result=True`, `errors=[]`. |
| `OK` | `Result[None]` constant | Trivial success for fail-or-succeed operations that carry no data. |
`Result` is **generic over `T` only** (not over the error type). Errors are
always `list[ErrorInfo]`. This is the AND-over-OR principle: data and errors
are parallel fields, not a tagged sum.
---
## Decision Tree
```
Need to represent "missing or failed"?
|
+-- Is the value a "data" value (not a control-flow signal)?
| +-- Use a Result dataclass (data + errors list)
| +-- Use a nil-sentinel dataclass (zero-initialized)
|
+-- Is the value a control-flow signal (e.g., "abort" or "skip")?
| +-- Use a boolean (or enum)
| +-- Use Optional[bool] / Optional[Enum] ONLY if the absence is meaningful
|
+-- Is the failure "unrecoverable" (programmer error, not runtime condition)?
| +-- Use assert (debug builds)
| +-- Use raise (only for programmer errors like KeyError on a known dict)
|
+-- Does the SDK raise an exception you can't avoid?
+-- Catch at the boundary; convert to ErrorInfo inside a Result
```
---
## Anti-Patterns
**DON'T do these things:**
1. **DON'T** use `Optional[X]` for "this might fail at runtime". Use
`Result[X]` instead.
2. **DON'T** use `None` as a sentinel for "no result". Use a nil-sentinel
dataclass.
3. **DON'T** raise a custom exception class for runtime failures. Catch SDK
exceptions and return `ErrorInfo`.
4. **DON'T** use `Union[T, E]` (sum type). Use a struct with parallel fields
(AND over OR).
5. **DON'T** have `if x is None: handle; else: use_x` patterns in production
code. The nil-sentinel makes them unnecessary.
6. **DON'T** catch `except Exception` and silently swallow. Convert to
`ErrorInfo` and return in the `Result`.
---
## Examples
The 3 refactored subsystems demonstrate each pattern in context:
- **`src/mcp_client.py:205-294`** — `read_file`, `list_directory`,
`search_files` return `Result[str]`; `(p, err)` tuples become
`Result[Path]`; the 30+ `assert p is not None` chain (lines 304-794) is
removed.
- **`src/ai_client.py`** — `_send_<vendor>_result()` returns `Result[str]`
(8 vendors: gemini, anthropic, deepseek, minimax, gemini_cli, qwen, llama,
grok); `send_result()` is the new public API; `send()` is `@deprecated`.
- **`src/rag_engine.py:100-180`** — `_init_vector_store_result`,
`_validate_collection_dim_result`, `is_empty_result`, `add_documents_result`
return `Result[None]` or `Result[T]`; broad `except Exception` blocks
become `ErrorInfo` entries.
---
## Hard Rules (enforced in the 3 refactored files)
These are non-negotiable in `src/mcp_client.py`, `src/ai_client.py`, and
`src/rag_engine.py`:
- **`Optional[T]` return types are FORBIDDEN** in the 3 refactored files. Use
`Result[T]` (with `NIL_T` singleton if needed) instead. Rationale:
`Optional[T]` is the sum type `Union[T, None]` that Fleury's framework
replaces. Mixing the two patterns reintroduces the bifurcation the
convention is designed to remove.
- **Function return types must be `Result[T]` for any function that can fail
at runtime.** A function that can't fail (e.g., `get_name() -> str`)
doesn't need a `Result`. The classification is "can this return a different
value under different runtime conditions?" If yes, `Result`. If no, plain
return type.
- **Catch SDK exceptions at the boundary only.** Inside the 3 refactored
files, the only place an exception is caught is at the SDK call site
(e.g., `_send_<vendor>_result()` wrapping the SDK call). Internal
`try/except` is reserved for converting `OSError`, `PermissionError`, and
similar I/O exceptions to `ErrorInfo` at the mcp_client tool boundary.
The verification script `scripts/audit_optional_in_3_files.py` enforces the
`Optional[X]` rule by failing CI if any new `Optional[X]` appears in the 3
refactored files.
### `Optional[X]` in argument types
The `Optional[X]` ban above applies to **return types only**. Argument types
that genuinely may be `None` (e.g., `rag_engine: Optional[Any] = None`,
`pre_tool_callback: Optional[Callable] = None`) remain allowed; they describe
a caller choice, not a runtime failure of this function.
### Cross-thread safety
`Result` and `ErrorInfo` are `@dataclass(frozen=True)` and therefore
thread-safe by immutability. The `with_error()` / `with_errors()` /
`with_data()` methods produce new instances (no mutation), matching the
project's "no shared mutable state across threads" invariant. Deprecation
warnings use `warnings.warn(..., stacklevel=2)` which is thread-safe.
---
## When to Use This Convention
**Use it for:**
- New public APIs (any function that can fail at runtime and the caller
might care).
- New internal functions where the caller benefits from knowing the failure
(vs. just propagating `None`).
**Don't use it for:**
- Constructors (`__init__`) that fail with programmer errors (use `assert` or
`raise` for these).
- Trivial getters that can't fail (`get_name() -> str` doesn't need a
`Result`).
- Performance-critical hot paths where the overhead of the dataclass
allocation is measurable (rare; benchmark first).
---
## Migration Playbook
When converting existing code:
1. Identify the `Optional[X]` return type or the `raise` statement.
2. Define a `Result` dataclass (or use the existing one) with `data: X` and
`errors: list[ErrorInfo]`.
3. Replace `None` returns with `Result(data=NIL_X, errors=[...])` or
`Result(data=zero_value, errors=[...])`.
4. Replace `raise X` with
`return Result(data=zero_value, errors=[ErrorInfo(kind=..., message=...)])`.
5. Update the caller to check `result.errors` instead of `is None` /
`try/except`.
6. Add a test that verifies both the success and failure paths return the
right `Result`.
---
## Deprecation: `ai_client.send()` → `ai_client.send_result()`
The public `ai_client.send()` is marked `@deprecated` (via
`typing_extensions.deprecated`, the Python 3.11+ backport of
`@warnings.deprecated`). It still works for backward compat but emits a
`DeprecationWarning` at runtime. New code MUST use `ai_client.send_result()`.
- `send_result(...) -> Result[str, ErrorInfo]` — the new public API.
- `send(...) -> str` — **deprecated.** Returns `str` for backward compat;
errors are logged to the comms log but not returned.
- Removal timeline: `public_api_migration_20260606` follow-up track.
The deprecation warning is cached per call site (Python's `__warningregistry__`)
to avoid log spam. `tests/conftest.py` adds a `filterwarnings` entry to
silence the warning during the transition; new tests for the new API should
assert the warning is NOT emitted by `send_result()`.
---
## See Also
- `conductor/tracks/data_oriented_error_handling_20260606/spec.md` — the spec
that established this convention.
- `docs/guide_ai_client.md` "Data-Oriented Error Handling (Fleury Pattern)"
— the in-context guide for the provider layer.
- `docs/guide_mcp_client.md` "Data-Oriented Error Handling (Fleury Pattern)"
— the in-context guide for the MCP tool layer.
- `docs/guide_rag.md` "Data-Oriented Error Handling (Fleury Pattern)" — the
in-context guide for the RAG engine.
- Ryan Fleury's [original article](https://www.dgtlgrove.com/p/the-easiest-way-to-handle-errors)
— the philosophical foundation.