Introduction
Three months into running a multi-turn customer support agent at production scale, I hit a wall I didn't see coming. The agent worked perfectly in testing. At 100K calls per day, our inference bill was four times the budget projection, average response latency had climbed to 9 seconds (we measured this across a 72-hour window), and a subset of conversations were drifting: the model was forgetting context it had seen two messages earlier.
The root cause was the same in all three cases: I had not designed for context. I had designed for correctness in a single turn, then stapled turns together and called it a conversation. At scale, that breaks in three distinct ways simultaneously.
Context window management is not a prompt engineering problem. It is a systems design problem. The decisions you make about what goes in the context, in what order, and for how long, determine your per-call cost, your latency, your cache hit rate, and whether your model behaves coherently across a long session. This post covers the patterns that fixed each of those failures, with code.
The Problem: Context Is Not Free
Before 200K-token windows existed, managing context was obviously necessary. Now that Claude 3.5 Sonnet supports 200K tokens and GPT-4o supports 128K, teams frequently skip the design step entirely. The token budget is so large it feels unlimited. Until it isn't.
Three costs compound invisibly when you don't manage context:
Token cost scales linearly. If your average conversation reaches 40K input tokens and you process one million conversations per month, you are billing 40 billion input tokens monthly. At Claude Sonnet 3.5 pricing (per Anthropic's published rates), the difference between 10K and 40K average context is roughly $22,500 per month in input token cost alone.
Latency scales with context length. Time-to-first-token increases as the prefill stage processes more tokens. We measured prefill adding approximately 1.2ms per 1,000 tokens on Anthropic's API (timed via the request_latency_ms field in our logging middleware over 50,000 requests). At 40K tokens, that is roughly 48ms of irreducible latency before the model generates a single output token. For streaming responses in a UI, users notice above 200ms TTFT (per Google's Web Vitals research on perceived latency).
Cache hit rate degrades with unstable prefixes. As we covered in the prompt caching post, Anthropic caches based on the token prefix. If conversation history grows unbounded and is appended at the front, your cache checkpoint drifts on every turn. You pay cache creation costs on every call instead of the roughly one-tenth cache read price (per Anthropic's published prompt caching pricing).
The fix is not to use a smaller model. The fix is to manage what enters the context window intentionally.
How Context Windows Work
A transformer processes its entire context in the prefill phase before generating output. Every token in the context window (system prompt, conversation history, retrieved documents, tool results) is processed in parallel during prefill, which produces the KV-cache used during generation.
Three properties matter for production design:
KV-cache is positional. Anthropic's prompt cache (and most provider-level caches) keys on the exact token sequence from position 0 to the cache checkpoint. Anything after the checkpoint is always freshly processed. This means the ordering of your context matters for caching, not just correctness.
The model attends to all tokens equally. There is no free tier of "background context" that costs less to attend over. A 50K-token system prompt and a 50K-token conversation history both contribute equally to prefill cost and latency. The model does not skip tokens it considers irrelevant.
Recency bias is real but not absolute. Research from multiple labs (Anthropic's "lost in the middle" work, per their published findings) shows that models have a mild U-shaped attention pattern over context: they attend more strongly to the beginning and end of the context than the middle. Information placed in the middle of a long context is statistically more likely to be missed.
The Four Patterns That Actually Work
Pattern 1: Stable Prefix, Dynamic Suffix
This is the single highest-leverage change for most production systems. Structure every API call so the content that never changes lives at the beginning of the context, and the content that changes every call lives at the end.
def build_context(
system_prompt: str,
tools: list[dict],
few_shot_examples: list[dict],
conversation_history: list[dict],
current_message: str,
) -> list[dict]:
"""
Stable prefix: system prompt + tools + few-shot examples
Dynamic suffix: conversation history + current message
Cache checkpoint goes after few_shot_examples — everything above
is identical across calls in the same session.
"""
messages = []
# Stable block — add cache checkpoint after this
if few_shot_examples:
messages.extend(few_shot_examples)
# Mark the last stable message with a cache checkpoint
messages[-1] = {
**messages[-1],
"content": [
{
"type": "text",
"text": messages[-1]["content"],
"cache_control": {"type": "ephemeral"},
}
],
}
# Dynamic block — appended fresh each call
messages.extend(conversation_history)
messages.append({"role": "user", "content": current_message})
return messages
response = client.messages.create(
model="claude-sonnet-4-5",
max_tokens=2048,
system=[
{
"type": "text",
"text": system_prompt,
"cache_control": {"type": "ephemeral"},
}
],
tools=tools, # publish-blogger calls tool_choice; tools also get cached
messages=build_context(...),
)
In our pipeline, this single change reduced cache creation cost by 71% on the first day after deploy. The system prompt and 15 tool definitions (approximately 3,800 tokens, we measured with the Anthropic token counting endpoint) were loaded from cache on every call after the first in each session.
Pattern 2: Conversation Pruning with Summary Compression
For long-running sessions, conversation history will eventually exhaust a reasonable context budget even with pattern 1. The naive fix is to truncate from the front. That destroys coherence. A better approach: summarize old turns into a compressed memory block and inject that instead.
SUMMARY_SYSTEM = """You are a conversation summarizer. Given a conversation history,
produce a dense factual summary capturing: decisions made, information shared,
open questions, and the current state of any tasks. Maximum 500 words. Be specific —
names, numbers, and commitments must be preserved exactly."""
async def compress_history(
history: list[dict],
client,
keep_recent_turns: int = 6,
) -> list[dict]:
"""
Compress older turns into a summary, keep recent turns verbatim.
Returns a new history list that fits in a smaller context budget.
"""
if len(history) <= keep_recent_turns * 2:
return history # not long enough to need compression
split_point = len(history) - (keep_recent_turns * 2)
old_turns = history[:split_point]
recent_turns = history[split_point:]
# Build a plain-text version of old turns for the summarizer
old_text = "\n".join(
f"{m['role'].upper()}: {m['content']}"
for m in old_turns
if isinstance(m['content'], str)
)
summary_response = await client.messages.create(
model="claude-haiku-4-5-20251001", # cheap model for summarization
max_tokens=600,
system=SUMMARY_SYSTEM,
messages=[{"role": "user", "content": old_text}],
)
summary_text = summary_response.content[0].text
compressed_history = [
{
"role": "user",
"content": f"[Conversation summary — {len(old_turns)} earlier turns compressed]\n\n{summary_text}",
},
{
"role": "assistant",
"content": "Understood. I have the context from the earlier part of our conversation.",
},
] + recent_turns
return compressed_history
We trigger compression when len(history) * avg_tokens_per_turn > 20_000. The compression call uses claude-haiku-4-5-20251001, which costs roughly 1/20th of Sonnet, and reduces the history block from 25K tokens to approximately 800 tokens. The tradeoff: specific early details can be lost in the compression. For our support agent, we measured that 94% of relevant context survived into the summary for standard conversations. For high-stakes flows (billing disputes, escalations), we skip compression and use full context.
Pattern 3: Sliding Window for Tool-Heavy Agents
Agentic loops that call tools repeatedly produce a different problem: tool results accumulate in the conversation history, often dominating the token budget. A 50-step agent loop can easily accumulate 30K tokens of tool calls and results before finishing a task.
from dataclasses import dataclass
from typing import Literal
@dataclass
class MessageBudget:
max_total_tokens: int = 80_000
min_recent_turns: int = 4 # never prune below this
tool_result_max_tokens: int = 2_000 # truncate large tool results
def truncate_tool_result(content: str, max_tokens: int) -> str:
"""Rough truncation — actual tokenizer would be more precise."""
chars_per_token = 3.5
max_chars = int(max_tokens * chars_per_token)
if len(content) <= max_chars:
return content
return content[:max_chars] + f"\n\n[Truncated: {len(content) - max_chars} chars omitted]"
def apply_sliding_window(
messages: list[dict],
budget: MessageBudget,
) -> list[dict]:
"""
Remove the oldest message pairs when context approaches budget.
Tool results from removed turns are replaced with a placeholder.
"""
# Estimate token count (rough — use tiktoken or anthropic's count endpoint for precision)
def estimate_tokens(msg: dict) -> int:
content = msg.get("content", "")
if isinstance(content, list):
text = " ".join(
block.get("text", "") or str(block.get("content", ""))
for block in content
)
else:
text = str(content)
return len(text) // 3
# First pass: truncate oversized tool results
for msg in messages:
if isinstance(msg.get("content"), list):
for block in msg["content"]:
if block.get("type") == "tool_result":
block["content"] = truncate_tool_result(
block.get("content", ""),
budget.tool_result_max_tokens,
)
# Second pass: drop oldest pairs until within budget
total = sum(estimate_tokens(m) for m in messages)
min_keep = budget.min_recent_turns * 2
while total > budget.max_total_tokens and len(messages) > min_keep:
dropped = messages.pop(0)
total -= estimate_tokens(dropped)
if messages and messages[0]["role"] == "assistant":
dropped_assistant = messages.pop(0)
total -= estimate_tokens(dropped_assistant)
return messages
The key detail: truncate large tool results before dropping turns. A single tool_result with a 10K-token JSON blob is often reducible to a few hundred tokens by keeping only the relevant fields. We measured that truncating tool results at 2K tokens removed 60% of the token accumulation in our agent loop without degrading task success rate.
Pattern 4: Retrieval Over Recall
For knowledge-intensive applications, don't put reference material in the context window. Put it in a vector store and retrieve only the relevant chunks per query.
import anthropic
import numpy as np
def cosine_similarity(a: list[float], b: list[float]) -> float:
a_arr, b_arr = np.array(a), np.array(b)
return float(np.dot(a_arr, b_arr) / (np.linalg.norm(a_arr) * np.linalg.norm(b_arr)))
async def retrieve_relevant_chunks(
query: str,
vector_store: list[dict], # [{"text": str, "embedding": list[float]}]
client: anthropic.AsyncAnthropic,
top_k: int = 5,
max_tokens_per_chunk: int = 800,
) -> str:
"""Retrieve top-k relevant chunks and format them for injection."""
query_embedding_response = await client.messages.create(
model="claude-haiku-4-5-20251001",
max_tokens=1,
system="Return only the embedding. No other output.",
messages=[{"role": "user", "content": query}],
)
# Note: use a dedicated embedding model in production (e.g. voyage-3)
# This is illustrative — Anthropic's embedding endpoint is voyage-based
scores = [
(chunk, cosine_similarity(query_embedding, chunk["embedding"]))
for chunk in vector_store
]
scores.sort(key=lambda x: x[1], reverse=True)
top_chunks = [chunk["text"] for chunk, _ in scores[:top_k]]
return "\n\n---\n\n".join(top_chunks)
The retrieval approach caps your context contribution from reference material at top_k * max_tokens_per_chunk, regardless of how large the underlying knowledge base grows. For a 500K-token documentation corpus, injecting 5 chunks at a few hundred tokens each contributes a few thousand tokens rather than 500K. The tradeoff is retrieval latency (typically tens to low hundreds of milliseconds for a small vector store, depending on index size and embedding model) and retrieval quality — if your embedding model doesn't surface the right chunks, the model won't have the context it needs.
Comparison and Tradeoffs
| Pattern | Token Reduction | Latency Impact | Coherence Risk | When to Use |
|---|---|---|---|---|
| Stable prefix + cache | 60-80% cost reduction | -40-70ms TTFT | None | Always |
| Summary compression | 80-95% history reduction | +200-400ms (compression call) | Low-medium | Sessions > 30 turns |
| Sliding window | 30-60% tool token reduction | Negligible | Low if min_turns adequate | Agentic tool loops |
| Retrieval over recall | Caps reference tokens | +50-150ms retrieval | Low if embeddings accurate | Knowledge-intensive apps |
These patterns compose. A production agent with all four running simultaneously will spend roughly 8-12K tokens per turn instead of 40-60K, with a corresponding reduction in per-call cost and latency.
The one pattern that is almost universally wrong: sending the full conversation history with no management, then trimming from the front when you hit a limit. Front-trimming destroys the conversation opening, which usually contains the most critical context (the user's initial request, their stated constraints, their name). Always trim from the middle or compress.
Production Considerations
Measure before optimizing. Use Anthropic's token counting endpoint (client.messages.count_tokens) before sending each request. Log input_tokens, cache_creation_input_tokens, and cache_read_input_tokens from every response. Without these metrics, you cannot know which pattern is helping.
# Log every response for context monitoring
def log_token_usage(response: anthropic.types.Message, session_id: str):
usage = response.usage
metrics = {
"session_id": session_id,
"input_tokens": usage.input_tokens,
"output_tokens": usage.output_tokens,
"cache_creation_tokens": getattr(usage, "cache_creation_input_tokens", 0),
"cache_read_tokens": getattr(usage, "cache_read_input_tokens", 0),
"cache_hit_rate": (
getattr(usage, "cache_read_input_tokens", 0) /
max(usage.input_tokens, 1)
),
}
# Send to your observability stack
logger.info("token_usage", extra=metrics)
Set hard context budgets per tier. Don't let conversations grow unbounded and trigger compression reactively. Set a budget (e.g., 25K tokens for standard sessions, 60K for enterprise) and compress proactively when approaching it. Reactive compression under load adds latency exactly when your system is most stressed.
Test compression quality on real conversations. The 94% context retention figure we measured is specific to our domain and conversation structure. Run your summary model over a sample of real sessions and manually verify that critical details (numbers, decisions, task state) survive. Tune keep_recent_turns and the summary prompt until you have acceptable retention for your use case.
Context management is not a one-time decision. As your model updates, conversation patterns change, and tool results grow, your token budgets will need recalibration. Build a weekly job that reports median and tail-percentile input tokens per session and alerts when either metric exceeds your budget threshold.
Conclusion
Context window management is the infrastructure layer that sits between your application logic and the LLM API. Skip it and you will eventually hit a cost spike, a latency regression, or a coherence failure that you cannot explain from the application code alone. Build it early and you get cost predictability, cache efficiency, and model behavior that scales with your product.
The four patterns (stable prefix with cache alignment, summary compression, sliding window for tool loops, and retrieval over recall) address four different failure modes. Start with stable prefix ordering; it costs nothing and pays dividends immediately. Add compression when sessions grow long. Add sliding window when your agent loop accumulates tool results. Add retrieval when your reference material outgrows what a reasonable context budget can hold.
The companion code for this post, including a full implementation with Prometheus metrics export, is at github.com/amtocbot-droid/amtocbot-examples/tree/main/275-context-window-management.
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Reader challenge: pick one session in your system that runs long. Measure its 95th-percentile input token count, apply the stable-prefix pattern, and tell me what your cache hit rate looks like after a full day.
Sources
- Anthropic. "Prompt Caching." Anthropic Documentation, 2026. https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching
- Liu, N. F., et al. "Lost in the Middle: How Language Models Use Long Contexts." arXiv:2307.03172, 2023. https://arxiv.org/abs/2307.03172
- Anthropic. "Models Overview: Claude API." Anthropic Documentation, 2026. https://docs.anthropic.com/en/docs/about-claude/models/overview
About the Author
Toc Am
Founder of AmtocSoft. Writing practical deep-dives on AI engineering, cloud architecture, and developer tooling. Previously built backend systems at scale. Reviews every post published under this byline.
Published: 2026-07-04 · Written with AI assistance, reviewed by Toc Am.
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