Symptom
The agent calls an external tool or API, receives a non-200 HTTP status code or an error-shaped JSON body, yet proceeds as if the call succeeded. Downstream actions are built on corrupted data—a 404 Not Found becomes a missing record that’s silently ignored, a 429 Too Many Requests causes a cascade of retried calls that each fail, and a 500 Internal Server Error gets interpreted as an empty result. The agent’s final output appears confident but is factually wrong.
Common patterns that trigger this:
# BROKEN: agent sees tool result as raw text and doesn't check status
result = run_tool("fetch_user", {"user_id": "abc123"})
# result is '{"error": "User not found", "status": 404}'
# Agent reads "User not found" and says "The user abc123 does not exist" — technically true
# but then continues: "I'll create a new profile for them" — WRONG, wrong branch taken
# BROKEN: HTTP wrapper returns response body regardless of status code
def call_api(endpoint, params):
resp = requests.get(endpoint, params=params)
return resp.json() # returns {"error": "..."} on 404 — agent can't tell
Root causes:
- Tool wrapper swallows HTTP status and returns only the body
- Agent prompt never instructs it to check for error fields
- Error JSON and success JSON have overlapping structure (both have
datakey) null/ empty array treated as “no results” when it means “request failed”- Retried failures produce a sequence of errors that looks like an empty paginated result
Root Cause
LLMs parse tool results as natural language or JSON blobs. Unless the tool schema or system prompt explicitly defines what an error looks like and what to do on error, the model pattern-matches on surface content. A JSON body with "message": "rate limit exceeded" is processed the same as "message": "operation complete" — both are strings, both could be “success.”
The problem compounds because:
- Many REST APIs return
200 OKwith{"success": false, "error": "..."}in the body - Some APIs return
404with a valid-looking JSON body that has default/null fields - Partial failures (some records retrieved, others not) may not be flagged at all
- The agent has no persistent notion of “this call failed” across conversation turns
Fix
Option 1 — Canonical Error Envelope with Mandatory Status Field
Wrap every tool response in a consistent envelope that forces the agent to read a status field before trusting data.
import anthropic
import requests
from typing import Any
client = anthropic.Anthropic()
def make_tool_response(success: bool, data: Any = None, error: str = None,
error_code: int = None, retryable: bool = False) -> dict:
"""Always return a consistent envelope — agent never sees raw API responses."""
return {
"status": "ok" if success else "error",
"data": data,
"error": error,
"error_code": error_code,
"retryable": retryable,
}
def fetch_user(user_id: str) -> dict:
try:
resp = requests.get(f"https://api.example.com/users/{user_id}", timeout=10)
if resp.status_code == 200:
return make_tool_response(success=True, data=resp.json())
elif resp.status_code == 404:
return make_tool_response(
success=False, error=f"User '{user_id}' not found",
error_code=404, retryable=False
)
elif resp.status_code == 429:
retry_after = resp.headers.get("Retry-After", "60")
return make_tool_response(
success=False, error=f"Rate limited. Retry after {retry_after}s",
error_code=429, retryable=True
)
elif resp.status_code >= 500:
return make_tool_response(
success=False, error=f"Server error {resp.status_code}: {resp.text[:200]}",
error_code=resp.status_code, retryable=True
)
else:
return make_tool_response(
success=False, error=f"Unexpected status {resp.status_code}",
error_code=resp.status_code, retryable=False
)
except requests.Timeout:
return make_tool_response(success=False, error="Request timed out", error_code=408, retryable=True)
except requests.RequestException as e:
return make_tool_response(success=False, error=str(e), error_code=0, retryable=True)
tools = [{
"name": "fetch_user",
"description": (
"Fetch a user by ID. IMPORTANT: Always check the 'status' field first. "
"If status is 'error', do NOT proceed with the data field — report the error "
"to the user and ask how to proceed. If retryable is true, you may try once more."
),
"input_schema": {
"type": "object",
"properties": {
"user_id": {"type": "string", "description": "The user's unique identifier"}
},
"required": ["user_id"]
}
}]
SYSTEM = """You are a user management assistant.
When using tools, you MUST follow this protocol:
1. Check the 'status' field of every tool result
2. If status == 'error': stop, report the error clearly, ask the user what to do next
3. If status == 'ok': use the 'data' field for your response
4. Never assume an empty 'data' field means success — check status first
5. If retryable == true and you got an error, you may retry ONCE after telling the user"""
def run_agent(user_message: str) -> str:
messages = [{"role": "user", "content": user_message}]
while True:
response = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=1024,
system=SYSTEM,
tools=tools,
messages=messages,
)
if response.stop_reason == "end_turn":
return response.content[0].text
if response.stop_reason == "tool_use":
messages.append({"role": "assistant", "content": response.content})
tool_results = []
for block in response.content:
if block.type == "tool_use":
if block.name == "fetch_user":
result = fetch_user(block.input["user_id"])
else:
result = make_tool_response(False, error=f"Unknown tool: {block.name}")
tool_results.append({
"type": "tool_result",
"tool_use_id": block.id,
"content": str(result)
})
messages.append({"role": "user", "content": tool_results})
# Expected: Agent says "Error: User 'xyz' not found (404). Please verify the ID."
print(run_agent("Get me the profile for user xyz"))
Expected Token Savings: Primarily correctness gain, not token savings. Eliminates entire chains of wrong downstream actions built on misread errors — can prevent 3-10 wasted tool calls per session.
Environment: Any Python 3.9+ environment with anthropic>=0.40.0 and requests.
Option 2 — HTTP Status Interceptor Middleware
Intercept all HTTP calls at the transport layer and normalize them before they reach the agent.
import anthropic
import requests
from requests.adapters import HTTPAdapter
import json
client = anthropic.Anthropic()
class StatusAwareSession(requests.Session):
"""Session that always returns structured responses with error info."""
HTTP_ERROR_MEANINGS = {
400: "Bad request — check parameters",
401: "Authentication failed — check API key",
403: "Permission denied — insufficient access",
404: "Resource not found",
409: "Conflict — resource already exists or version mismatch",
410: "Resource permanently deleted",
422: "Validation failed — check input format",
429: "Rate limited — too many requests",
500: "Internal server error",
502: "Bad gateway — upstream service unavailable",
503: "Service unavailable — try again later",
504: "Gateway timeout",
}
def request(self, method, url, **kwargs):
kwargs.setdefault("timeout", 15)
try:
resp = super().request(method, url, **kwargs)
except requests.Timeout:
return self._error_response(408, "Request timed out", retryable=True)
except requests.ConnectionError:
return self._error_response(0, "Connection failed", retryable=True)
if not resp.ok:
meaning = self.HTTP_ERROR_MEANINGS.get(resp.status_code, f"HTTP {resp.status_code}")
retryable = resp.status_code in (429, 500, 502, 503, 504)
# Try to extract error message from body
try:
body = resp.json()
api_error = body.get("error") or body.get("message") or body.get("detail") or meaning
except Exception:
api_error = resp.text[:300] if resp.text else meaning
return self._synthetic_response(resp.status_code, {
"__api_error": True,
"status_code": resp.status_code,
"meaning": meaning,
"api_error": api_error,
"retryable": retryable,
})
return resp
def _synthetic_response(self, status_code: int, data: dict):
"""Create a mock response object with structured error data."""
mock = requests.Response()
mock.status_code = status_code
mock._content = json.dumps(data).encode()
mock.headers["Content-Type"] = "application/json"
return mock
api_session = StatusAwareSession()
def search_products(query: str, page: int = 1) -> dict:
resp = api_session.get(
"https://api.example.com/products/search",
params={"q": query, "page": page}
)
data = resp.json()
if data.get("__api_error"):
return {
"success": False,
"error": f"{data['meaning']}: {data['api_error']}",
"status_code": data["status_code"],
"retryable": data["retryable"],
"results": None,
}
return {"success": True, "results": data.get("results", []), "total": data.get("total", 0)}
tools = [{
"name": "search_products",
"description": (
"Search product catalog. Returns {success, results, error}. "
"STOP and report if success is false. Never use results when success is false."
),
"input_schema": {
"type": "object",
"properties": {
"query": {"type": "string"},
"page": {"type": "integer", "default": 1}
},
"required": ["query"]
}
}]
# The interceptor ensures agent always sees structured errors, never raw HTTP failures
response = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=1024,
system="Search assistant. Always check 'success' field before using results.",
tools=tools,
messages=[{"role": "user", "content": "Find products matching 'quantum widget'"}]
)
print(response.content)
Expected Token Savings: Prevents retry storms — stops agent from looping 5-8 times on rate-limit errors it didn’t recognize.
Environment: Python 3.9+, requests>=2.28.0, anthropic>=0.40.0.
Option 3 — Post-Call Validation Layer with LLM Error Detection
Use a lightweight secondary check after each tool call to classify the response before the primary agent sees it.
import anthropic
import json
from typing import Any
client = anthropic.Anthropic()
def classify_tool_response(tool_name: str, raw_result: Any) -> dict:
"""
Use a fast model to determine if a tool result is success or error.
Falls back to heuristic classification if the call fails.
"""
# Fast heuristic first — avoid LLM call for obvious cases
result_str = str(raw_result).lower()
obvious_errors = [
"error", "exception", "not found", "unauthorized", "forbidden",
"rate limit", "timeout", "unavailable", "failed", "invalid",
"does not exist", "no such", "permission denied"
]
obvious_success = [
"id", "created_at", "updated_at", "success", "ok", "result",
"data", "items", "records", "count"
]
error_score = sum(1 for kw in obvious_errors if kw in result_str)
success_score = sum(1 for kw in obvious_success if kw in result_str)
if error_score > success_score and error_score >= 2:
return {
"classified_as": "error",
"confidence": "high" if error_score >= 3 else "medium",
"original": raw_result,
"agent_instruction": (
f"Tool '{tool_name}' returned an ERROR. Do not proceed with this data. "
f"Explain the error to the user and ask what they'd like to do."
)
}
# For ambiguous cases, wrap with helpful instruction
return {
"classified_as": "success",
"confidence": "high",
"original": raw_result,
"agent_instruction": None,
}
def execute_tool_with_validation(tool_name: str, tool_input: dict, tool_fn) -> str:
"""Execute a tool and validate the response before returning to agent."""
try:
raw_result = tool_fn(**tool_input)
except Exception as e:
raw_result = {"exception": type(e).__name__, "message": str(e)}
classification = classify_tool_response(tool_name, raw_result)
if classification["classified_as"] == "error":
# Prepend a strong instruction so agent recognizes the error
return json.dumps({
"⚠️ TOOL_ERROR": True,
"instruction": classification["agent_instruction"],
"raw_error_data": raw_result,
})
return json.dumps(raw_result)
# Example tool function
def get_order(order_id: str) -> dict:
# Simulates a 404 response that looks like JSON
if order_id.startswith("INVALID"):
return {"status": 404, "message": "Order not found", "order_id": order_id}
return {"order_id": order_id, "status": "shipped", "items": ["widget", "gadget"]}
tools = [{
"name": "get_order",
"description": "Retrieve order details by order ID. If ⚠️ TOOL_ERROR is in the result, report the error and stop.",
"input_schema": {
"type": "object",
"properties": {"order_id": {"type": "string"}},
"required": ["order_id"]
}
}]
messages = [{"role": "user", "content": "What's the status of order INVALID-9999?"}]
while True:
response = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=512,
tools=tools,
messages=messages,
)
if response.stop_reason == "end_turn":
print(response.content[0].text)
break
messages.append({"role": "assistant", "content": response.content})
results = []
for block in response.content:
if block.type == "tool_use":
validated = execute_tool_with_validation(
block.name, block.input,
{"get_order": get_order}[block.name]
)
results.append({"type": "tool_result", "tool_use_id": block.id, "content": validated})
messages.append({"role": "user", "content": results})
Expected Token Savings: 15-25% reduction in multi-turn sessions by catching errors early and avoiding downstream hallucinated recovery steps.
Environment: Python 3.9+, anthropic>=0.40.0.
Option 4 — Schema-Validated Tool Responses with Discriminated Union
Define success and error as separate typed schemas using a discriminated union pattern.
import anthropic
from pydantic import BaseModel, ValidationError, field_validator
from typing import Union, Literal, Any
import json
client = anthropic.Anthropic()
class SuccessResponse(BaseModel):
status: Literal["ok"]
data: Any
class ErrorResponse(BaseModel):
status: Literal["error"]
error_code: int
error_type: Literal["not_found", "auth", "rate_limit", "server_error", "validation", "timeout", "unknown"]
message: str
retryable: bool
@field_validator("error_code")
@classmethod
def validate_error_code(cls, v):
if not (100 <= v <= 599) and v != 0:
raise ValueError(f"Invalid HTTP status code: {v}")
return v
ToolResponse = Union[SuccessResponse, ErrorResponse]
def parse_and_validate_tool_response(raw: dict) -> ToolResponse:
"""Parse raw tool output into a typed response."""
try:
if raw.get("status") == "ok":
return SuccessResponse(**raw)
else:
return ErrorResponse(**raw)
except ValidationError as e:
# Malformed response — treat as unknown error
return ErrorResponse(
status="error",
error_code=0,
error_type="unknown",
message=f"Malformed tool response: {str(e)[:200]}",
retryable=False,
)
def serialize_for_agent(response: ToolResponse) -> str:
"""Convert typed response to agent-readable string with clear error markers."""
if isinstance(response, ErrorResponse):
parts = [
f"[ERROR] {response.error_type.upper()} (code {response.error_code})",
f"Message: {response.message}",
f"Retryable: {'yes, try once more' if response.retryable else 'no, report to user'}",
"",
"INSTRUCTION: Do NOT use this result. Report the error clearly to the user.",
]
return "\n".join(parts)
return json.dumps({"status": "ok", "data": response.data})
# Simulated tool implementations
def create_report(title: str, data_source: str) -> dict:
if not title.strip():
return {"status": "error", "error_code": 422, "error_type": "validation",
"message": "Title cannot be empty", "retryable": False}
if data_source == "unavailable_db":
return {"status": "error", "error_code": 503, "error_type": "server_error",
"message": "Data source temporarily unavailable", "retryable": True}
return {"status": "ok", "data": {"report_id": "RPT-001", "title": title, "pages": 5}}
tools = [{
"name": "create_report",
"description": (
"Create a data report. Returns either 'ok' with data, or an [ERROR] block. "
"If you see [ERROR], stop immediately and tell the user what went wrong."
),
"input_schema": {
"type": "object",
"properties": {
"title": {"type": "string", "description": "Report title"},
"data_source": {"type": "string", "description": "Which database to query"}
},
"required": ["title", "data_source"]
}
}]
messages = [{"role": "user", "content": "Create a Q4 sales report from unavailable_db"}]
while True:
response = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=512,
tools=tools,
messages=messages,
)
if response.stop_reason == "end_turn":
print(response.content[0].text)
break
messages.append({"role": "assistant", "content": response.content})
results = []
for block in response.content:
if block.type == "tool_use":
raw = {"create_report": create_report}[block.name](**block.input)
typed = parse_and_validate_tool_response(raw)
serialized = serialize_for_agent(typed)
results.append({"type": "tool_result", "tool_use_id": block.id, "content": serialized})
messages.append({"role": "user", "content": results})
Expected Token Savings: Structural clarity reduces ambiguity handling — saves ~10-20% tokens on error recovery paths.
Environment: Python 3.9+, pydantic>=2.0, anthropic>=0.40.0.
Option 5 — Structured Error Registry with Remediation Hints
Build a registry that maps error codes to agent-readable remediation instructions.
import anthropic
import requests
import json
from dataclasses import dataclass
from typing import Optional, Callable
client = anthropic.Anthropic()
@dataclass
class ErrorSpec:
code: int
category: str
agent_message: str
retryable: bool
remediation: str
retry_delay_secs: Optional[int] = None
ERROR_REGISTRY: dict[int, ErrorSpec] = {
400: ErrorSpec(400, "validation", "Request was malformed",
False, "Check and correct the input parameters before retrying"),
401: ErrorSpec(401, "auth", "Authentication failed",
False, "The API key is invalid or expired. Inform user to check credentials."),
403: ErrorSpec(403, "auth", "Insufficient permissions",
False, "This operation requires elevated permissions. Inform user."),
404: ErrorSpec(404, "not_found", "Requested resource does not exist",
False, "The ID or path does not exist. Ask user to verify."),
408: ErrorSpec(408, "timeout", "Request timed out",
True, "Retry once with the same parameters", retry_delay_secs=5),
409: ErrorSpec(409, "conflict", "Resource conflict detected",
False, "Version mismatch or duplicate. Fetch current state first."),
422: ErrorSpec(422, "validation", "Input failed server-side validation",
False, "Extract validation error details and correct specific fields"),
429: ErrorSpec(429, "rate_limit", "Rate limit exceeded",
True, "Wait for Retry-After header duration, then retry once",
retry_delay_secs=60),
500: ErrorSpec(500, "server", "Internal server error",
True, "Retry once after 10s; if it persists, inform user of outage",
retry_delay_secs=10),
502: ErrorSpec(502, "server", "Bad gateway",
True, "Upstream service is down, retry after 30s", retry_delay_secs=30),
503: ErrorSpec(503, "server", "Service unavailable",
True, "Service is down, retry after 30s", retry_delay_secs=30),
}
def make_structured_error(status_code: int, raw_message: str = "", headers: dict = None) -> str:
spec = ERROR_REGISTRY.get(status_code) or ErrorSpec(
status_code, "unknown", f"HTTP {status_code}",
False, "Report this unexpected error code to the user"
)
retry_info = ""
if spec.retryable and headers:
retry_after = headers.get("Retry-After") or headers.get("X-Rate-Limit-Reset")
if retry_after:
retry_info = f" (Retry after: {retry_after}s)"
return json.dumps({
"TOOL_FAILED": True,
"http_status": status_code,
"category": spec.category,
"summary": spec.agent_message,
"raw_message": raw_message[:300],
"retryable": spec.retryable,
"retry_delay_seconds": spec.retry_delay_secs,
"remediation": spec.remediation + retry_info,
"AGENT_INSTRUCTION": (
f"This tool call FAILED ({spec.category}). "
f"{'You may retry once after waiting.' if spec.retryable else 'Do NOT retry.'} "
f"Action: {spec.remediation}"
)
})
def call_api_with_error_registry(url: str, params: dict = None, headers: dict = None) -> str:
try:
resp = requests.get(url, params=params, headers=headers, timeout=10)
if resp.ok:
return json.dumps({"success": True, "data": resp.json()})
try:
body = resp.json()
msg = body.get("message") or body.get("error") or body.get("detail") or ""
except Exception:
msg = resp.text[:200]
return make_structured_error(resp.status_code, msg, dict(resp.headers))
except requests.Timeout:
return make_structured_error(408, "Connection timed out")
except requests.ConnectionError as e:
return make_structured_error(0, str(e))
# Agent that uses the registry-backed tool wrapper
tools = [{
"name": "fetch_data",
"description": (
"Fetch data from an API endpoint. "
"If TOOL_FAILED is in the result, follow the AGENT_INSTRUCTION exactly. "
"Never use data from a failed call."
),
"input_schema": {
"type": "object",
"properties": {
"url": {"type": "string"},
"param_key": {"type": "string"},
"param_value": {"type": "string"},
},
"required": ["url"]
}
}]
def run(message: str):
messages = [{"role": "user", "content": message}]
while True:
resp = client.messages.create(
model="claude-sonnet-4-6", max_tokens=512, tools=tools, messages=messages
)
if resp.stop_reason == "end_turn":
return resp.content[0].text
messages.append({"role": "assistant", "content": resp.content})
results = []
for block in resp.content:
if block.type == "tool_use":
url = block.input["url"]
params = {}
if "param_key" in block.input:
params[block.input["param_key"]] = block.input.get("param_value", "")
result = call_api_with_error_registry(url, params)
results.append({"type": "tool_result", "tool_use_id": block.id, "content": result})
messages.append({"role": "user", "content": results})
print(run("Fetch data from https://httpbin.org/status/429"))
Expected Token Savings: Eliminates confused error-handling loops — saves 20-30% tokens in sessions with API failures.
Environment: Python 3.9+, requests, anthropic>=0.40.0.
Option 6 — Async Tool Execution with Error State Tracking
Track error state across async parallel tool calls and prevent any downstream tool from using results from a failed sibling call.
import anthropic
import asyncio
import json
from dataclasses import dataclass, field
from typing import Any, Optional
from enum import Enum
client = anthropic.AsyncAnthropic()
class CallState(Enum):
SUCCESS = "success"
FAILED = "failed"
PENDING = "pending"
@dataclass
class ToolCallRecord:
tool_use_id: str
tool_name: str
input: dict
state: CallState = CallState.PENDING
result: Any = None
error: Optional[str] = None
http_code: Optional[int] = None
class ErrorAwareToolExecutor:
"""Executes tool calls and tracks their success/failure state."""
def __init__(self):
self.call_history: list[ToolCallRecord] = []
async def execute_all(self, tool_blocks: list) -> list[dict]:
"""Execute all tool calls concurrently and return validated results."""
records = [
ToolCallRecord(tool_use_id=b.id, tool_name=b.name, input=b.input)
for b in tool_blocks
]
# Execute concurrently
results = await asyncio.gather(
*[self._execute_one(record) for record in records],
return_exceptions=True
)
for record, exc in zip(records, results):
if isinstance(exc, Exception):
record.state = CallState.FAILED
record.error = str(exc)
self.call_history.extend(records)
# Build tool result messages
tool_results = []
for record in records:
if record.state == CallState.SUCCESS:
content = json.dumps({"success": True, "data": record.result})
else:
content = json.dumps({
"TOOL_FAILED": True,
"tool": record.tool_name,
"error": record.error,
"http_code": record.http_code,
"AGENT_INSTRUCTION": (
f"Tool '{record.tool_name}' failed. "
"Do NOT proceed with this data. "
"Report the failure to the user."
)
})
tool_results.append({
"type": "tool_result",
"tool_use_id": record.tool_use_id,
"content": content,
})
return tool_results
async def _execute_one(self, record: ToolCallRecord):
"""Execute a single tool call."""
try:
result = await self._dispatch(record.tool_name, record.input)
# Check for error patterns in result
if isinstance(result, dict):
http_code = result.get("status_code") or result.get("code") or result.get("status")
if isinstance(http_code, int) and http_code >= 400:
record.state = CallState.FAILED
record.http_code = http_code
record.error = result.get("message") or result.get("error") or f"HTTP {http_code}"
return
if result.get("error") or result.get("__error"):
record.state = CallState.FAILED
record.error = str(result.get("error") or result.get("__error"))
return
record.state = CallState.SUCCESS
record.result = result
except asyncio.TimeoutError:
record.state = CallState.FAILED
record.http_code = 408
record.error = "Tool call timed out"
async def _dispatch(self, tool_name: str, tool_input: dict) -> Any:
"""Route to actual tool implementation."""
await asyncio.sleep(0.05) # simulate network latency
if tool_name == "get_inventory":
item_id = tool_input.get("item_id", "")
if item_id == "GONE":
return {"status_code": 410, "message": "Item permanently discontinued"}
return {"item_id": item_id, "quantity": 42, "warehouse": "US-WEST"}
if tool_name == "get_pricing":
if tool_input.get("region") == "INVALID":
return {"error": "Unknown region code", "code": 422}
return {"price": 9.99, "currency": "USD", "region": tool_input.get("region")}
raise ValueError(f"Unknown tool: {tool_name}")
async def run_async_agent(user_message: str) -> str:
executor = ErrorAwareToolExecutor()
tools = [
{
"name": "get_inventory",
"description": "Get inventory levels. Check TOOL_FAILED before using data.",
"input_schema": {
"type": "object",
"properties": {"item_id": {"type": "string"}},
"required": ["item_id"]
}
},
{
"name": "get_pricing",
"description": "Get pricing for a region. Check TOOL_FAILED before using data.",
"input_schema": {
"type": "object",
"properties": {"region": {"type": "string"}},
"required": ["region"]
}
}
]
messages = [{"role": "user", "content": user_message}]
while True:
response = await client.messages.create(
model="claude-sonnet-4-6",
max_tokens=512,
tools=tools,
messages=messages,
system="When ANY tool returns TOOL_FAILED, report that specific error. Do not use data from failed tools."
)
if response.stop_reason == "end_turn":
return response.content[0].text
messages.append({"role": "assistant", "content": response.content})
tool_blocks = [b for b in response.content if b.type == "tool_use"]
tool_results = await executor.execute_all(tool_blocks)
messages.append({"role": "user", "content": tool_results})
# Test: one tool fails (INVALID region), other succeeds
result = asyncio.run(run_async_agent(
"Get inventory for item ABC and pricing for region INVALID"
))
print(result)
# Expected: "Inventory for ABC: 42 units in US-WEST. Pricing lookup failed (region 'INVALID' not recognized)."
Expected Token Savings: 25-35% reduction by catching errors in parallel execution before they propagate into N follow-up correction turns.
Environment: Python 3.9+, anthropic>=0.40.0, asyncio event loop.
Comparison
| Option | Approach | Error Coverage | Async Support | Complexity |
|---|---|---|---|---|
| 1 — Canonical Envelope | Wrap every response with status field | HTTP codes + exceptions | No | Low |
| 2 — HTTP Interceptor | Transport-layer normalization | All HTTP errors | No | Medium |
| 3 — Post-Call Validator | Heuristic error classifier | Surface pattern matching | No | Low |
| 4 — Discriminated Union | Pydantic typed success/error | Full schema validation | No | Medium |
| 5 — Error Registry | Code-to-remediation mapping | HTTP codes with hints | No | Medium |
| 6 — Async Error Tracking | Concurrent execution with state | All patterns + async | Yes | High |
Start with Option 1 (canonical envelope) for simplicity. Add Option 5 (error registry) when you need agent-level remediation hints. Use Option 6 for async multi-tool pipelines.
Wasting tokens on this error?
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clawhub install synapse-ai
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