Symptom
Two problems from the same root: (1) the agent truncates a 200-line function mid-generation because max_tokens=256, leaving broken code; (2) the agent answers “yes” with max_tokens=4096 — you paid for 4000 potential tokens on a binary response. Both indicate a hardcoded max_tokens that ignores what the task actually needs.
Root Cause
max_tokens is set once at agent initialization and applied identically to every call. Short Q&A, long code generation, document summarization, and one-word confirmations all use the same budget. This creates a trilemma: set it too low (truncation), too high (wasted cost), or accept that some tasks will be wrong.
Fix
Option 1: Task-Type Router with Pre-Defined Token Budgets
Classify the request type and select an appropriate max_tokens budget.
import re
import anthropic
client = anthropic.Anthropic()
# Token budgets by task type
TASK_BUDGETS = {
"yes_no": 32, # Binary answers
"short_answer": 128, # One-paragraph answers
"explanation": 512, # Conceptual explanations
"list": 512, # Bullet point lists
"code_snippet": 1024, # Small functions, scripts
"code_file": 4096, # Full file generation
"document": 8192, # Reports, essays, docs
"analysis": 2048, # Detailed analysis
"translation": 2048, # Language translation
"default": 1024,
}
# Patterns for task detection (order matters — more specific first)
TASK_PATTERNS = [
("yes_no", r"\b(yes or no|true or false|is it|does it|can you confirm|are you)\b"),
("code_file", r"\b(full file|entire (file|module|class|application)|complete implementation|write a (full|complete))\b"),
("code_snippet", r"\b(function|method|class|snippet|example|script|implement|write (a|the|some) code)\b"),
("document", r"\b(essay|report|article|document|blog post|write (a|an|the) \d{3,})\b"),
("analysis", r"\b(analyze|analyse|explain in detail|deep dive|comprehensive|thorough)\b"),
("translation", r"\b(translate|in (spanish|french|german|japanese|chinese|korean))\b"),
("list", r"\b(list|enumerate|give me \d+|top \d+|bullet points?)\b"),
("explanation", r"\b(what is|how does|explain|describe|tell me about)\b"),
]
def classify_task(user_message: str) -> tuple[str, int]:
"""Classify message and return (task_type, max_tokens)."""
msg_lower = user_message.lower()
for task_type, pattern in TASK_PATTERNS:
if re.search(pattern, msg_lower, re.IGNORECASE):
return task_type, TASK_BUDGETS[task_type]
return "default", TASK_BUDGETS["default"]
def adaptive_chat(user_message: str, system: str = "You are a helpful assistant.") -> tuple[str, str, int]:
"""
Chat with automatically selected max_tokens.
Returns (response, task_type, max_tokens_used).
"""
task_type, max_tokens = classify_task(user_message)
print(f" [Task: {task_type}, max_tokens: {max_tokens}]")
response = client.messages.create(
model="claude-haiku-4-5-20251001",
max_tokens=max_tokens,
system=system,
messages=[{"role": "user", "content": user_message}],
)
actual_output = response.usage.output_tokens
efficiency = actual_output / max_tokens * 100
print(f" [Used {actual_output}/{max_tokens} tokens ({efficiency:.0f}% utilization)]")
return response.content[0].text, task_type, max_tokens
# Test with varied tasks
test_cases = [
"Is Python a dynamically typed language?",
"List the top 5 Python web frameworks.",
"Write a Python function to sort a list of dicts by a key.",
"Explain how Python's GIL works.",
"Write a complete Python module for a REST API client with authentication, retry logic, and rate limiting.",
]
for msg in test_cases:
print(f"\nQ: {msg[:70]}")
reply, task, tokens = adaptive_chat(msg)
print(f"A: {reply[:100]}...")
Expected Token Savings: Short Q&A at max_tokens=32 vs 4096 saves ~99% of potential cost for those calls. Across mixed workloads, expect 40–70% savings vs a uniform high budget.
Environment: Pure Python, no dependencies. Tune TASK_BUDGETS based on your workload distribution.
Option 2: Input-Length-Based Budget Estimation
Estimate required output tokens from the input length and task characteristics.
import anthropic
client = anthropic.Anthropic()
# Typical output:input ratios by task type
OUTPUT_INPUT_RATIOS = {
"summarization": 0.25, # Output is ~25% of input length
"translation": 1.10, # Output ≈ input (slight expansion)
"code_from_spec": 5.0, # Spec → code: 5× expansion typical
"qa": 0.5, # Answer ≈ half the question length
"expansion": 3.0, # Expand brief to full: 3× expansion
}
MIN_TOKENS = 64
MAX_TOKENS = 8192
def estimate_max_tokens(
user_message: str,
task_type: str = "qa",
context_documents: list[str] | None = None,
safety_multiplier: float = 1.3,
) -> int:
"""
Estimate max_tokens based on input length and task type.
Adds a safety margin to avoid truncation.
"""
# Estimate input tokens (rough: 4 chars ≈ 1 token)
message_tokens = len(user_message) // 4
doc_tokens = sum(len(doc) // 4 for doc in (context_documents or []))
total_input_tokens = message_tokens + doc_tokens
ratio = OUTPUT_INPUT_RATIOS.get(task_type, 1.0)
estimated_output = int(total_input_tokens * ratio * safety_multiplier)
# Clamp to bounds
return max(MIN_TOKENS, min(MAX_TOKENS, estimated_output))
def smart_create(
user_message: str,
task_type: str = "qa",
context_documents: list[str] | None = None,
model: str = "claude-haiku-4-5-20251001",
) -> anthropic.types.Message:
"""Create with estimated max_tokens based on task and input size."""
max_tokens = estimate_max_tokens(user_message, task_type, context_documents)
print(f" [Estimated max_tokens: {max_tokens} for task_type={task_type}]")
messages = [{"role": "user", "content": user_message}]
if context_documents:
context = "\n\n".join(f"Document {i+1}:\n{doc}" for i, doc in enumerate(context_documents))
messages = [{"role": "user", "content": f"Context:\n{context}\n\nQuestion: {user_message}"}]
response = client.messages.create(
model=model,
max_tokens=max_tokens,
messages=messages,
)
actual = response.usage.output_tokens
if actual >= max_tokens * 0.95:
print(f" ⚠️ Response near token limit ({actual}/{max_tokens}). Consider increasing budget.")
else:
print(f" ✓ Used {actual}/{max_tokens} tokens ({actual/max_tokens*100:.0f}%)")
return response
# Summarization: short output
long_doc = "Machine learning is a subset of artificial intelligence... " * 50
r = smart_create(
"Summarize the key points of this document.",
task_type="summarization",
context_documents=[long_doc],
)
print(r.content[0].text[:100])
# Code generation from spec: needs more tokens
spec = "Function that: validates email, checks domain DNS, rate-limits to 100/hour, logs attempts"
r = smart_create(spec, task_type="code_from_spec")
print(r.content[0].text[:200])
Expected Token Savings: Input-proportional estimation prevents systematic over-allocation. Summarization of a 2000-token doc uses max_tokens=650 instead of 4096 — 84% savings. Environment: No dependencies. Calibrate ratios from your actual workload samples.
Option 3: Streaming + Early Stop on Completion Detection
Stream the response. Stop when the output is semantically complete, rather than waiting for max_tokens.
import anthropic
import re
client = anthropic.Anthropic()
# Signals that indicate a complete response
COMPLETION_SIGNALS = [
r"\n```\s*$", # Closed code block
r"\n\d+\.\s+[A-Z].*\.\s*$", # Numbered list end
r"(In summary|To summarize|In conclusion)[^.]*\.\s*$", # Conclusion
r"Hope this helps!?\s*$", # Conversational end
r"\n---\s*$", # Horizontal rule
]
MAX_TOKENS_HARD_LIMIT = 4096 # Never go above this
EARLY_STOP_CHECK_EVERY = 50 # Check completion every N tokens
def streaming_with_smart_stop(
user_message: str,
model: str = "claude-haiku-4-5-20251001",
) -> tuple[str, int]:
"""
Stream with semantic completion detection.
Returns (full_text, tokens_used).
"""
collected = []
total_tokens = 0
stopped_early = False
with client.messages.stream(
model=model,
max_tokens=MAX_TOKENS_HARD_LIMIT,
messages=[{"role": "user", "content": user_message}],
) as stream:
for token in stream.text_stream:
collected.append(token)
total_tokens += 1 # rough token count
# Periodically check for completion signals
if total_tokens % EARLY_STOP_CHECK_EVERY == 0:
current_text = "".join(collected)
for pattern in COMPLETION_SIGNALS:
if re.search(pattern, current_text, re.MULTILINE):
stopped_early = True
print(f" [Early stop at ~{total_tokens} tokens] Completion signal detected")
# Note: can't actually stop the Anthropic stream mid-response
# but we can stop processing. The SDK will continue until stop_reason.
break
full_text = "".join(collected)
actual_tokens = stream.get_final_message().usage.output_tokens if not stopped_early else total_tokens
return full_text, actual_tokens
# Dynamic max_tokens based on question complexity heuristic
def heuristic_max_tokens(message: str) -> int:
words = len(message.split())
has_code_request = any(kw in message.lower() for kw in ["write", "implement", "code", "function", "class"])
has_list_request = any(kw in message.lower() for kw in ["list", "enumerate", "give me"])
has_detail_request = any(kw in message.lower() for kw in ["detail", "explain", "comprehensive"])
base = 256
if has_code_request: base = 2048
if has_list_request: base = max(base, 512)
if has_detail_request: base = max(base, 1024)
# Scale slightly with question length
base = int(base * (1 + words / 200))
return min(base, MAX_TOKENS_HARD_LIMIT)
def smart_stream(message: str) -> str:
max_t = heuristic_max_tokens(message)
print(f" [Heuristic max_tokens: {max_t}]")
collected = []
with client.messages.stream(
model="claude-haiku-4-5-20251001",
max_tokens=max_t,
messages=[{"role": "user", "content": message}],
) as stream:
for token in stream.text_stream:
collected.append(token)
print(token, end="", flush=True)
print()
final = stream.get_final_message()
used = final.usage.output_tokens
print(f" [Used {used}/{max_t} tokens]")
return "".join(collected)
for msg in [
"Is Redis faster than PostgreSQL for caching? Yes or no.",
"List 5 Python testing frameworks.",
"Write a Python class for a thread-safe LRU cache.",
]:
print(f"\nQ: {msg}")
smart_stream(msg)
Expected Token Savings: Heuristic sizing prevents 4096-token budgets on yes/no questions. Streaming allows accurate utilization measurement for tuning. Environment: Streaming SDK. Cannot truly stop mid-stream on Anthropic’s API — but heuristic sizing achieves the same cost reduction.
Option 4: Tiered Model + Token Budget Co-Selection
Select both the model AND the token budget together based on task complexity. Simple tasks get Haiku + small budget; complex tasks get Sonnet + large budget.
import anthropic
from dataclasses import dataclass
client = anthropic.Anthropic()
@dataclass
class TaskProfile:
task_type: str
model: str
max_tokens: int
description: str
TASK_PROFILES = {
"trivial": TaskProfile(
task_type="trivial",
model="claude-haiku-4-5-20251001",
max_tokens=64,
description="Yes/no, simple lookups, date conversions",
),
"simple": TaskProfile(
task_type="simple",
model="claude-haiku-4-5-20251001",
max_tokens=256,
description="Short explanations, simple Q&A",
),
"moderate": TaskProfile(
task_type="moderate",
model="claude-haiku-4-5-20251001",
max_tokens=1024,
description="Code snippets, lists, explanations",
),
"complex": TaskProfile(
task_type="complex",
model="claude-sonnet-4-6",
max_tokens=4096,
description="Multi-step reasoning, full code files, analysis",
),
"intensive": TaskProfile(
task_type="intensive",
model="claude-sonnet-4-6",
max_tokens=8192,
description="Long documents, complex architecture, research",
),
}
PROFILE_SELECTOR_TOOL = {
"name": "select_task_profile",
"description": "Select the appropriate task profile for this request.",
"input_schema": {
"type": "object",
"properties": {
"profile": {
"type": "string",
"enum": list(TASK_PROFILES.keys()),
"description": "Task complexity profile",
},
"reasoning": {"type": "string"},
},
"required": ["profile", "reasoning"],
},
}
# Use tiny Haiku call to classify before the main call
_classifier = anthropic.Anthropic()
def select_profile(user_message: str) -> TaskProfile:
"""Use a cheap Haiku call to classify the task and select the right profile."""
response = _classifier.messages.create(
model="claude-haiku-4-5-20251001",
max_tokens=128,
tools=[PROFILE_SELECTOR_TOOL],
tool_choice={"type": "any"},
system=(
"Classify the complexity of this request and select a task profile.\n"
"trivial: yes/no questions\n"
"simple: short factual answers\n"
"moderate: paragraphs, short code\n"
"complex: multi-file code, detailed analysis\n"
"intensive: long documents, comprehensive research"
),
messages=[{"role": "user", "content": user_message}],
)
for block in response.content:
if block.type == "tool_use" and block.name == "select_task_profile":
profile_name = block.input["profile"]
return TASK_PROFILES[profile_name]
return TASK_PROFILES["moderate"] # safe default
def tiered_create(user_message: str, system: str = "You are a helpful assistant.") -> str:
"""Select model + max_tokens based on task profile, then execute."""
profile = select_profile(user_message)
print(f" [Profile: {profile.task_type}] model={profile.model}, max_tokens={profile.max_tokens}")
print(f" [Description: {profile.description}]")
response = client.messages.create(
model=profile.model,
max_tokens=profile.max_tokens,
system=system,
messages=[{"role": "user", "content": user_message}],
)
actual = response.usage.output_tokens
print(f" [Used {actual}/{profile.max_tokens} tokens]")
return response.content[0].text
# Test
requests = [
"Is 17 a prime number?",
"What is the difference between TCP and UDP?",
"Write a Python class implementing a binary search tree with insert, search, and delete.",
"Design a complete microservices architecture for an e-commerce platform with 50,000 daily active users.",
]
for req in requests:
print(f"\nRequest: {req[:80]}")
reply = tiered_create(req)
print(f"Reply: {reply[:150]}...")
Expected Token Savings: Trivial tasks at Haiku+64 tokens cost ~$0.000001. Same task at Opus+4096 costs ~$0.006. For 1000 trivial queries/day: ~$6/day saved. Environment: Two API calls per request (classifier + main). Classification costs ~20 Haiku tokens — pays back immediately on any non-trivial routing.
Option 5: Adaptive Budget with Utilization Feedback Loop
Track utilization per task type. Automatically adjust budgets based on actual usage patterns.
import json
import sqlite3
import time
import anthropic
client = anthropic.Anthropic()
# Persistent utilization tracking
perf_conn = sqlite3.connect("token_budgets.db")
perf_conn.execute("""
CREATE TABLE IF NOT EXISTS utilization (
task_type TEXT,
budget INTEGER,
used INTEGER,
ts REAL
)
""")
perf_conn.commit()
# Starting budgets
BUDGETS = {
"qa": 512,
"code": 2048,
"doc": 4096,
"summary": 1024,
}
LEARN_RATE = 0.1 # How fast to adjust (0=no adjust, 1=full replace)
MIN_BUDGET = 64
MAX_BUDGET = 8192
UTILIZATION_TARGET = 0.75 # Target 75% utilization
def record_utilization(task_type: str, budget: int, used: int):
perf_conn.execute(
"INSERT INTO utilization VALUES (?,?,?,?)",
(task_type, budget, used, time.time())
)
perf_conn.commit()
def get_recommended_budget(task_type: str) -> int:
"""
Get budget adjusted from recent utilization data.
If p90 usage is below 60% of budget → shrink.
If p90 usage exceeds 90% of budget → grow.
"""
rows = perf_conn.execute(
"SELECT used, budget FROM utilization WHERE task_type=? ORDER BY ts DESC LIMIT 50",
(task_type,),
).fetchall()
if len(rows) < 5:
return BUDGETS.get(task_type, 1024) # Not enough data yet
usages = sorted([r[0] for r in rows])
p90_used = usages[int(len(usages) * 0.9)]
current_budget = BUDGETS.get(task_type, 1024)
util_rate = p90_used / current_budget
if util_rate < 0.6:
# Shrink budget
new_budget = int(current_budget * (1 - LEARN_RATE * (0.6 - util_rate)))
elif util_rate > 0.9:
# Grow budget (add 20% headroom above p90)
new_budget = int(p90_used * 1.2)
else:
new_budget = current_budget
new_budget = max(MIN_BUDGET, min(MAX_BUDGET, new_budget))
if new_budget != current_budget:
BUDGETS[task_type] = new_budget
print(f" [Budget updated] {task_type}: {current_budget} → {new_budget} (p90={p90_used})")
return new_budget
def adaptive_create(
user_message: str,
task_type: str = "qa",
model: str = "claude-haiku-4-5-20251001",
) -> str:
max_tokens = get_recommended_budget(task_type)
print(f" [Adaptive budget] {task_type}: max_tokens={max_tokens}")
response = client.messages.create(
model=model,
max_tokens=max_tokens,
messages=[{"role": "user", "content": user_message}],
)
used = response.usage.output_tokens
record_utilization(task_type, max_tokens, used)
print(f" [Utilization] {used}/{max_tokens} ({used/max_tokens*100:.0f}%)")
if response.stop_reason == "max_tokens":
print(f" ⚠️ TRUNCATED — budget too small for this response")
return response.content[0].text
# Simulate usage over time — budgets auto-adjust
qa_questions = [
"What is Python?",
"Name three sorting algorithms.",
"What is a REST API?",
"Define machine learning.",
"What does CPU stand for?",
]
for q in qa_questions[:3]:
print(f"\nQ: {q}")
adaptive_create(q, "qa")
# After a few uses, budget recommendation is calibrated
print(f"\n[Final QA budget recommendation: {get_recommended_budget('qa')}]")
Expected Token Savings: Self-calibrating budgets converge to actual usage. Over time, over-budgeted task types shrink automatically without manual tuning. Environment: SQLite utilization log. Resets BUDGETS dict in memory — persist to DB for production.
Option 6: Max-Tokens Negotiation via Structured Request
Let the model declare how many tokens it estimates it needs before generating.
import anthropic
client = anthropic.Anthropic()
NEGOTIATE_TOOL = {
"name": "declare_token_estimate",
"description": "Estimate how many output tokens you'll need to fully answer this request.",
"input_schema": {
"type": "object",
"properties": {
"estimated_tokens": {
"type": "integer",
"description": "Realistic token count needed for a complete answer",
},
"response_type": {
"type": "string",
"enum": ["one_word", "sentence", "paragraph", "list", "code", "long_form"],
},
"can_be_shorter": {
"type": "boolean",
"description": "True if you could give a useful but shorter answer if needed",
},
},
"required": ["estimated_tokens", "response_type", "can_be_shorter"],
},
}
CEILING = 8192 # Hard maximum we'll ever allow
FLOOR = 32
def negotiated_create(
user_message: str,
model: str = "claude-haiku-4-5-20251001",
user_max: int | None = None,
) -> str:
"""
Phase 1: Model estimates token needs (cheap call).
Phase 2: Generate with the negotiated budget.
"""
# Phase 1: Negotiate
negotiation = client.messages.create(
model=model,
max_tokens=128, # Only need a small response for the estimate
tools=[NEGOTIATE_TOOL],
tool_choice={"type": "any"},
messages=[{
"role": "user",
"content": (
f"Before answering, estimate how many tokens you'll need.\n\n"
f"Request: {user_message}"
),
}],
)
estimate = 1024 # default
for block in negotiation.content:
if block.type == "tool_use" and block.name == "declare_token_estimate":
estimate = block.input["estimated_tokens"]
resp_type = block.input["response_type"]
can_shorten = block.input["can_be_shorter"]
print(f" [Negotiated] estimate={estimate}, type={resp_type}, can_shorten={can_shorten}")
# Apply constraints
actual_max = max(FLOOR, min(CEILING, estimate))
if user_max:
actual_max = min(actual_max, user_max)
if estimate > user_max:
print(f" [Budget constrained] Model wanted {estimate}, user cap is {user_max}")
print(f" [Final max_tokens: {actual_max}]")
# Phase 2: Generate with negotiated budget
response = client.messages.create(
model=model,
max_tokens=actual_max,
messages=[{"role": "user", "content": user_message}],
)
actual_used = response.usage.output_tokens
print(f" [Used {actual_used}/{actual_max} tokens]")
return response.content[0].text
# Test
for msg in [
"What year was Python created?",
"Write a complete Python implementation of a B-tree data structure.",
"Give me a bullet list of 5 REST API best practices.",
]:
print(f"\nQ: {msg[:80]}")
reply = negotiated_create(msg)
print(f"A: {reply[:150]}...")
Expected Token Savings: Model self-estimates 45 tokens for a year answer vs your hardcoded 512. Budget negotiation reduces average over-allocation by 30–60% across mixed workloads. Environment: Two API calls (negotiate + generate). Total overhead: ~30 Haiku tokens for estimation. Best for high-volume, mixed-complexity workloads.
| Option | Approach | Latency Added | Self-Adjusting | Best For |
|---|---|---|---|---|
| 1 | Task-type routing table | None | No | Predictable task types |
| 2 | Input-length ratio | None | No | Proportional tasks (summarization) |
| 3 | Streaming + heuristic | None | No | Real-time feedback on utilization |
| 4 | Tiered model + budget | +1 classifier call | No | Cost-quality co-optimization |
| 5 | Utilization feedback loop | None | Yes | Production with historical data |
| 6 | Model self-estimation | +1 negotiate call | No | Unknown/mixed task distributions |
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