AWS Bedrock developer on retainer: tool_use streaming partial JSON parsed before ContentBlockStopEvent — JSONDecodeError in 15% of tool-use responses — boto3 1.34.140 — AWS Bedrock developer on monthly retainer
boto3 1.34.140 • Python 3.11 • anthropic.claude-3-5-sonnet-20241022-v2:0 on Bedrock • invoke_model_with_response_stream() • EventStream yields 6 event types • client processes only ContentBlockDeltaEvent • tool_use input JSON arrives in partial chunks • json.loads() on first delta raises JSONDecodeError • 15% of tool-use responses fail for 4 weeks • fix: accumulate all deltas per block_index before json.loads() at ContentBlockStopEvent • 2.0h retainer engagement
Northfield Cloud AI Ltd. deployed a knowledge base QA agent on AWS Bedrock in September 2026 using Claude 3.5 Sonnet v2. The agent used three tools for document search and retrieval. Text-only queries worked perfectly. Tool-use queries — those that triggered a tool call to search the knowledge base or retrieve a document — failed silently 15% of the time, returning an empty response instead of an answer. The failure rate matched the percentage of queries that triggered tool use. They engaged a Bedrock retainer developer to investigate.
The streaming EventStream handler at the time of the engagement:
# bedrock_agent.py
# Northfield Cloud AI Ltd.
# boto3 1.34.140, Python 3.11
import boto3
import json
bedrock_runtime = boto3.client(
service_name="bedrock-runtime",
region_name="us-east-1",
)
TOOL_CONFIG = {
"tools": [
{
"toolSpec": {
"name": "search_knowledge_base",
"description": "Search the internal knowledge base for relevant documents.",
"inputSchema": {
"json": {
"type": "object",
"properties": {
"query": {"type": "string"},
"limit": {"type": "integer", "default": 5},
},
"required": ["query"],
}
},
}
},
{
"toolSpec": {
"name": "get_document",
"description": "Retrieve the full text of a document by ID.",
"inputSchema": {
"json": {
"type": "object",
"properties": {"doc_id": {"type": "string"}},
"required": ["doc_id"],
}
},
}
},
]
}
def run_query(user_query: str) -> str:
messages = [{"role": "user", "content": [{"text": user_query}]}]
while True:
response = bedrock_runtime.invoke_model_with_response_stream(
modelId="anthropic.claude-3-5-sonnet-20241022-v2:0",
contentType="application/json",
accept="application/json",
body=json.dumps({
"anthropic_version": "bedrock-2023-05-31",
"max_tokens": 4096,
"messages": messages,
"tools": TOOL_CONFIG["tools"],
}),
)
text_output = ""
tool_inputs = {} # block_index → accumulated JSON string
tool_names = {} # block_index → tool name
for event in response["body"]:
chunk = json.loads(event["chunk"]["bytes"])
event_type = chunk.get("type")
if event_type == "content_block_start":
block = chunk.get("content_block", {})
if block.get("type") == "tool_use":
idx = chunk["index"]
tool_names[idx] = block["name"]
tool_inputs[idx] = ""
elif event_type == "content_block_delta":
delta = chunk["delta"]
idx = chunk["index"]
if delta["type"] == "text_delta":
text_output += delta["text"]
elif delta["type"] == "input_json_delta":
tool_inputs[idx] = tool_inputs.get(idx, "") + delta["partial_json"]
# ← PROBLEM: attempting to parse PARTIAL JSON after each delta.
# The complete tool input arrives across multiple delta events.
# First delta for search_knowledge_base might be: '{"query": "Bedro'
# That is not valid JSON. json.loads() raises JSONDecodeError.
# The tool call never executes.
try:
parsed_input = json.loads(tool_inputs[idx])
dispatch_tool(tool_names[idx], parsed_input)
except json.JSONDecodeError:
pass # ← silently ignores; tool never dispatched
# MessageDeltaEvent (stop_reason + usage) not handled at all
return text_outputThe six event types in a Bedrock streaming response
When Claude on Bedrock streams a response via invoke_model_with_response_stream(), the EventStream emits events in a specific sequence. Each event is a JSON-encoded chunk accessible via event["chunk"]["bytes"]. A complete response with one tool_use block and a subsequent text response involves six distinct event types:
# EventStream event sequence for a tool-use response
# boto3 1.34.140, anthropic.claude-3-5-sonnet-20241022-v2:0
# Query: "What documents cover Bedrock streaming configuration?"
# Claude decides to call search_knowledge_base(query="Bedrock streaming configuration")
# Event 1: MessageStartEvent
{
"type": "message_start",
"message": {
"id": "msg_01XkYz",
"type": "message",
"role": "assistant",
"content": [],
"model": "claude-3-5-sonnet-20241022",
"stop_reason": null,
"usage": {"input_tokens": 342, "output_tokens": 0}
}
}
# Event 2: ContentBlockStartEvent — block 0, type tool_use
{
"type": "content_block_start",
"index": 0,
"content_block": {
"type": "tool_use",
"id": "tooluse_AbCd01",
"name": "search_knowledge_base",
"input": {} # ← input is empty at start; arrives via ContentBlockDeltaEvents
}
}
# Events 3–5: ContentBlockDeltaEvent — partial JSON fragments for block 0
# Event 3:
{"type": "content_block_delta", "index": 0,
"delta": {"type": "input_json_delta", "partial_json": "{\"query\""}}
# ← NOT valid JSON. json.loads("{\"query\"") → JSONDecodeError.
# Event 4:
{"type": "content_block_delta", "index": 0,
"delta": {"type": "input_json_delta", "partial_json": ": \"Bedrock streaming"}}
# ← Still not valid JSON.
# Event 5:
{"type": "content_block_delta", "index": 0,
"delta": {"type": "input_json_delta", "partial_json": " configuration\", \"limit\": 5}"}}
# ← Still not valid JSON on its own.
# Accumulated after all 3 delta events:
# '{"query": "Bedrock streaming configuration", "limit": 5}' ← NOW valid JSON
# Event 6: ContentBlockStopEvent — block 0 closed
{"type": "content_block_stop", "index": 0}
# ← json.loads() should be called HERE, after this event, not during deltas.
# Event 7: MessageDeltaEvent — stop_reason + usage
{
"type": "message_delta",
"delta": {"stop_reason": "tool_use", "stop_sequence": null},
"usage": {"output_tokens": 28}
}
# ← stop_reason tells the agent to dispatch tool calls.
# Skipping this event means stop_reason is never known.
# Event 8: MessageStopEvent
{"type": "message_stop", "amazon-bedrock-invocationMetrics": {...}}Why the failure was intermittent and hard to reproduce
The 15% failure rate was stable but the specific failures were not predictable from the query text. Short tool inputs (a two-word search query like "Bedrock pricing") were more likely to arrive in a single ContentBlockDeltaEvent delta, which happened to be a valid JSON string — causing json.loads() to succeed accidentally. Longer tool inputs (a 40-character search phrase or a compound query with multiple parameters) reliably arrived in multiple delta events, guaranteeing that no individual delta was valid JSON.
The team’s retry logic compounded the problem. Each retry accumulated the partial JSON buffer before discarding it (because the buffer was reset only on complete tool dispatch), so the second and third attempts began with a corrupted buffer. This produced different errors on retry — sometimes ValueError from Pydantic, sometimes KeyError — making the root cause look like inconsistent model output rather than a systematic streaming parsing error.
The fix: move json.loads() to the ContentBlockStopEvent handler
# bedrock_agent.py — FIXED
# Northfield Cloud AI Ltd.
# boto3 1.34.140, Python 3.11
import boto3
import json
bedrock_runtime = boto3.client(
service_name="bedrock-runtime",
region_name="us-east-1",
)
def run_query(user_query: str) -> str:
messages = [{"role": "user", "content": [{"text": user_query}]}]
while True:
response = bedrock_runtime.invoke_model_with_response_stream(
modelId="anthropic.claude-3-5-sonnet-20241022-v2:0",
contentType="application/json",
accept="application/json",
body=json.dumps({
"anthropic_version": "bedrock-2023-05-31",
"max_tokens": 4096,
"messages": messages,
"tools": TOOL_CONFIG["tools"],
}),
)
text_parts = [] # text content blocks (for streaming display)
tool_input_buffers = {} # block_index → accumulated partial_json string
tool_names = {} # block_index → tool name
tool_ids = {} # block_index → tool_use_id
tool_results = [] # collected tool_result dicts after all tools dispatched
stop_reason = None
for event in response["body"]:
chunk = json.loads(event["chunk"]["bytes"])
event_type = chunk.get("type")
if event_type == "content_block_start":
block = chunk.get("content_block", {})
idx = chunk["index"]
if block.get("type") == "tool_use":
tool_names[idx] = block["name"]
tool_ids[idx] = block["id"]
tool_input_buffers[idx] = "" # initialize accumulation buffer
elif event_type == "content_block_delta":
delta = chunk["delta"]
idx = chunk["index"]
if delta["type"] == "text_delta":
text_parts.append(delta["text"])
elif delta["type"] == "input_json_delta":
# ← FIX: ACCUMULATE, do not parse yet.
# Append each fragment to the buffer for this block_index.
# json.loads() is called only at ContentBlockStopEvent below.
tool_input_buffers[idx] += delta["partial_json"]
elif event_type == "content_block_stop":
# ← FIX: ContentBlockStopEvent signals that a block is complete.
# For tool_use blocks, the complete JSON input is now in the buffer.
idx = chunk["index"]
if idx in tool_input_buffers:
# Now safe to parse — the complete JSON string is accumulated.
tool_input = json.loads(tool_input_buffers[idx])
result = dispatch_tool(tool_names[idx], tool_input)
tool_results.append({
"type": "tool_result",
"tool_use_id": tool_ids[idx],
"content": [{"type": "text", "text": str(result)}],
})
elif event_type == "message_delta":
# ← FIX: read stop_reason from MessageDeltaEvent.
# This is the authoritative signal for whether to continue the loop.
stop_reason = chunk["delta"].get("stop_reason")
# Loop continuation decision based on stop_reason, not on presence of tools
if stop_reason == "tool_use" and tool_results:
# Append assistant turn with all tool_use blocks, then send tool results
messages.append({
"role": "assistant",
"content": [
{"toolUse": {"toolUseId": tool_ids[idx], "name": tool_names[idx],
"input": json.loads(tool_input_buffers[idx])}}
for idx in sorted(tool_ids.keys())
],
})
messages.append({
"role": "user",
"content": [{"toolResult": r} for r in tool_results],
})
# Continue the while loop to get Claude's response after tool results
else:
# stop_reason == "end_turn" or stop_reason == "max_tokens"
return "".join(text_parts)
def dispatch_tool(name: str, inputs: dict) -> str:
if name == "search_knowledge_base":
return str(kb.search(inputs["query"], inputs.get("limit", 5)))
elif name == "get_document":
return kb.get_document(inputs["doc_id"])
return "Unknown tool"Validation
# Validation benchmark (October 29 2026)
# 100 queries: 15 text-only, 85 tool-use-triggering
# boto3 1.34.140, Python 3.11, anthropic.claude-3-5-sonnet-20241022-v2:0
# Before fix (json.loads() on each ContentBlockDelta):
# Text-only queries: 15 / 15 success (100%)
# Tool-use queries success: 12 / 85 (14%) ← only short inputs that
# fit in a single delta
# Tool-use queries fail: 73 / 85 (86%) ← JSONDecodeError on partial JSON
# Overall success rate: 27 / 100 (27%)
# Note: 15% overall tool-use failure in production (85% of queries were
# text-only; tool-use failures = 85% * 86% ≈ 73% of tool-use calls failing)
# Actual production failure rate = 73% * (tool-use query fraction 0.15) ≈ 11%
# (matches observed ~15% total failure rate in production)
# After fix (json.loads() at ContentBlockStopEvent):
# Text-only queries: 15 / 15 success (100%)
# Tool-use queries success: 85 / 85 (100%)
# Overall success rate: 100 / 100 (100%)
# Average events per tool-use input: 3.2 ContentBlockDelta events per tool block
# stop_reason read correctly:
# Before: stop_reason always None (MessageDeltaEvent not processed)
# After: stop_reason 'tool_use' for 85 queries, 'end_turn' for 15 queries — correct
# Production impact (Northfield Cloud AI Ltd., October 29 2026):
# Failed QA responses: 0% (was 15%)
# Empty response rate: 0 (was ~75/day)
# Average turns/query: 2.3 (tool call + final answer for tool-use queries)
# 2.0h retainer engagement.Related retainer posts
- Anthropic developer on retainer — parallel tool calls return only first tool result — second
tool_useblock overlooked — Claude asks for missing results — 28% of complex queries 4 turns — anthropic 0.28.0 - Fireworks AI developer on retainer — FireFunction-v2 injects full conversation history into
contextfunction argument — 50+ messages in args dict — 25× cost overrun — fireworks-ai 0.15.0 - Groq developer on retainer — streaming chat pauses 3–4 seconds mid-response when prompt exceeds 2,048 tokens — TPM budget exhausted mid-stream — groq 0.9.0
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