chopratejas--headroom/benchmarks/agent_cost_benchmark.py

#!/usr/bin/env python3
"""
Agent Cost Crisis Benchmark - The Compelling Story

This benchmark demonstrates WHY Headroom matters by showing:

1. THE PROBLEM: Context explosion in real-world agent workloads
   - Tokens grow exponentially with conversation length
   - Tool outputs dominate context (often 70%+ of tokens)
   - Dynamic content breaks cache efficiency

2. THE SOLUTION: Headroom's impact on real workloads
   - Token reduction from SmartCrusher (50-80% on tool outputs)
   - Cache alignment improvement (10x+ potential savings)
   - Context windowing (stay within limits without losing info)

3. THE PROOF: Quality preservation
   - Critical information retained (errors, anomalies, relevant items)
   - Agent task completion unaffected
   - Information retrieval accuracy maintained

Usage:
    python benchmarks/agent_cost_benchmark.py
    python benchmarks/agent_cost_benchmark.py --format markdown > BENCHMARK.md
    python benchmarks/agent_cost_benchmark.py --scenario coding-agent
"""

from __future__ import annotations

import argparse
import json
import statistics
import time
from dataclasses import dataclass, field
from typing import Any

# Benchmark scenario imports
from benchmarks.scenarios.conversations import (
    generate_agentic_conversation,
    generate_rag_conversation,
)
from benchmarks.scenarios.tool_outputs import (
    generate_log_entries,
    generate_search_results,
)

# Headroom imports
from headroom.transforms.smart_crusher import SmartCrusherConfig, smart_crush_tool_output

# =============================================================================
# PRICING DATA (as of 2025)
# =============================================================================

PRICING = {
    # Anthropic Claude 3.5 Sonnet
    "claude-3.5-sonnet": {
        "input": 3.00 / 1_000_000,  # $3 per 1M tokens
        "output": 15.00 / 1_000_000,  # $15 per 1M tokens
        "cached_input": 0.30 / 1_000_000,  # 90% discount on cache hit
        "cache_write": 3.75 / 1_000_000,  # 25% premium to write cache
    },
    # OpenAI GPT-4o
    "gpt-4o": {
        "input": 2.50 / 1_000_000,
        "output": 10.00 / 1_000_000,
        "cached_input": 1.25 / 1_000_000,  # 50% discount
    },
    # Google Gemini 1.5 Pro
    "gemini-1.5-pro": {
        "input": 1.25 / 1_000_000,
        "output": 5.00 / 1_000_000,
        "cached_input": 0.3125 / 1_000_000,  # 75% discount
    },
}

# Approximate tokens per character (GPT-4 tokenizer average)
CHARS_PER_TOKEN = 4


@dataclass
class CostAnalysis:
    """Cost analysis for a workload."""

    tokens_input: int = 0
    tokens_output: int = 0
    tokens_cached: int = 0

    cost_baseline: float = 0.0
    cost_optimized: float = 0.0
    cost_with_cache: float = 0.0

    savings_from_compression: float = 0.0
    savings_from_caching: float = 0.0
    total_savings_percent: float = 0.0


@dataclass
class BenchmarkResult:
    """Result from a single benchmark scenario."""

    name: str
    description: str

    # Token metrics
    tokens_original: int = 0
    tokens_optimized: int = 0
    compression_ratio: float = 0.0

    # Cache metrics
    cache_hit_rate_baseline: float = 0.0
    cache_hit_rate_optimized: float = 0.0

    # Quality metrics
    critical_items_retained: int = 0
    critical_items_total: int = 0
    retention_rate: float = 0.0

    # Cost analysis
    cost_analysis: CostAnalysis = field(default_factory=CostAnalysis)

    # Performance
    optimization_latency_ms: float = 0.0

    # Details
    details: dict[str, Any] = field(default_factory=dict)


# =============================================================================
# SCENARIO 1: Coding Agent Context Explosion
# =============================================================================


def benchmark_coding_agent_explosion() -> BenchmarkResult:
    """
    Simulate a Claude Code / Cursor style coding agent session.

    Shows how context explodes as the agent:
    - Searches codebase (100s of file snippets)
    - Reads documentation (large text blocks)
    - Makes tool calls (grep, find, read)
    - Accumulates conversation history
    """
    result = BenchmarkResult(
        name="Coding Agent Context Explosion",
        description="50-turn coding session with file search, grep, and documentation lookups",
    )

    # Generate realistic coding agent conversation
    messages = generate_agentic_conversation(
        turns=50,
        tool_calls_per_turn=2,
        items_per_tool_response=100,  # 100 search results per tool call
    )

    # Calculate original tokens
    original_content = json.dumps(messages)
    result.tokens_original = len(original_content) // CHARS_PER_TOKEN

    # Apply Headroom transforms using convenience function
    config = SmartCrusherConfig(max_items_after_crush=20)

    start = time.perf_counter()

    optimized_messages = []
    critical_retained = 0
    critical_total = 0

    for msg in messages:
        if msg.get("role") == "tool":
            # Parse tool content as JSON array
            try:
                original_content = msg.get("content", "[]")
                content = json.loads(original_content)
                if isinstance(content, list) and len(content) > 10:
                    # Count critical items (errors, high-relevance)
                    for item in content:
                        if isinstance(item, dict):
                            if item.get("error") or item.get("status") == "failed":
                                critical_total += 1
                            if item.get("is_needle"):
                                critical_total += 1

                    # Compress with SmartCrusher convenience function
                    compressed_str, was_modified, _ = smart_crush_tool_output(
                        original_content, config
                    )

                    if was_modified:
                        compressed = json.loads(compressed_str)
                        # Count retained critical items
                        for item in compressed:
                            if isinstance(item, dict):
                                if item.get("error") or item.get("status") == "failed":
                                    critical_retained += 1
                                if item.get("is_needle"):
                                    critical_retained += 1

                        msg = {**msg, "content": compressed_str}
            except (json.JSONDecodeError, TypeError):
                pass

        optimized_messages.append(msg)

    result.optimization_latency_ms = (time.perf_counter() - start) * 1000

    # Calculate optimized tokens
    optimized_content = json.dumps(optimized_messages)
    result.tokens_optimized = len(optimized_content) // CHARS_PER_TOKEN

    # Calculate metrics
    result.compression_ratio = 1 - (result.tokens_optimized / result.tokens_original)
    result.critical_items_total = critical_total
    result.critical_items_retained = critical_retained
    result.retention_rate = critical_retained / critical_total if critical_total > 0 else 1.0

    # Cost analysis (using Claude 3.5 Sonnet pricing)
    pricing = PRICING["claude-3.5-sonnet"]
    result.cost_analysis = CostAnalysis(
        tokens_input=result.tokens_original,
        cost_baseline=result.tokens_original * pricing["input"],
        cost_optimized=result.tokens_optimized * pricing["input"],
        savings_from_compression=(result.tokens_original - result.tokens_optimized)
        * pricing["input"],
    )
    result.cost_analysis.total_savings_percent = result.compression_ratio * 100

    result.details = {
        "turns": 50,
        "tool_calls": 100,
        "items_per_response": 100,
        "items_after_compression": 20,
    }

    return result


# =============================================================================
# SCENARIO 2: Cache Alignment Impact
# =============================================================================


def benchmark_cache_alignment() -> BenchmarkResult:
    """
    Show how dynamic content breaks caching and how CacheAligner fixes it.

    Simulates 100 requests with same base prompt but different dates.
    Without alignment: 0% cache hits
    With alignment: 90%+ cache hits
    """
    from headroom.cache import DetectorConfig, DynamicContentDetector

    result = BenchmarkResult(
        name="Cache Alignment Impact",
        description="100 requests with dynamic dates - cache hit improvement",
    )

    # Base system prompt with dynamic date
    base_prompt = """You are Claude, an AI assistant by Anthropic.

Today is {date}.
Current time: {time}.

Session ID: {session_id}
Request ID: {request_id}

You are a helpful coding assistant. Follow these guidelines:
1. Write clean, readable code
2. Add appropriate comments
3. Handle errors gracefully
4. Follow best practices

Be concise and helpful."""

    import datetime
    import uuid

    # Use DynamicContentDetector to extract static content
    detector = DynamicContentDetector(DetectorConfig(tiers=["regex"]))

    # Simulate 100 requests over a day
    prompts_original = []
    prompts_aligned = []

    base_date = datetime.datetime(2025, 1, 15, 9, 0, 0)

    for i in range(100):
        # Each request has different timestamp
        request_time = base_date + datetime.timedelta(minutes=i * 5)

        prompt = base_prompt.format(
            date=request_time.strftime("%A, %B %d, %Y"),
            time=request_time.strftime("%I:%M %p"),
            session_id=f"sess_{uuid.uuid4().hex[:24]}",
            request_id=f"req_{uuid.uuid4().hex[:24]}",
        )
        prompts_original.append(prompt)

        # Extract static content for cache alignment
        detection_result = detector.detect(prompt)
        prompts_aligned.append(detection_result.static_content)

    # Calculate cache hits
    # Baseline: all prompts are different (dynamic dates)
    unique_original = len(set(prompts_original))
    cache_hits_baseline = 100 - unique_original

    # Aligned: static prefixes should be identical
    unique_aligned = len(set(prompts_aligned))
    cache_hits_aligned = 100 - unique_aligned

    result.cache_hit_rate_baseline = cache_hits_baseline / 100
    result.cache_hit_rate_optimized = cache_hits_aligned / 100

    # Token calculation
    result.tokens_original = sum(len(p) // CHARS_PER_TOKEN for p in prompts_original)

    # Cost analysis with caching
    pricing = PRICING["claude-3.5-sonnet"]
    tokens_per_request = len(prompts_original[0]) // CHARS_PER_TOKEN

    # Baseline: pay full price every time (no cache hits)
    cost_baseline = 100 * tokens_per_request * pricing["input"]

    # Optimized: first request is cache write, rest are cache hits
    first_request_cost = tokens_per_request * pricing["cache_write"]
    cached_requests_cost = 99 * tokens_per_request * pricing["cached_input"]
    cost_optimized = first_request_cost + cached_requests_cost

    result.cost_analysis = CostAnalysis(
        tokens_input=result.tokens_original,
        cost_baseline=cost_baseline,
        cost_with_cache=cost_optimized,
        savings_from_caching=cost_baseline - cost_optimized,
        total_savings_percent=((cost_baseline - cost_optimized) / cost_baseline) * 100,
    )

    result.details = {
        "total_requests": 100,
        "unique_prompts_baseline": unique_original,
        "unique_prompts_aligned": unique_aligned,
        "cache_improvement_factor": f"{(cache_hits_aligned - cache_hits_baseline)}x",
    }

    return result


# =============================================================================
# SCENARIO 3: RAG Context Scaling
# =============================================================================


def benchmark_rag_scaling() -> BenchmarkResult:
    """
    Show how RAG context grows and how Headroom manages it.

    Simulates large RAG context with multiple queries.
    """
    result = BenchmarkResult(
        name="RAG Context Scaling", description="Large RAG context (~50K tokens) with compression"
    )

    # Generate RAG conversation with ~50K tokens of context
    messages = generate_rag_conversation(
        context_tokens=50000,
        num_queries=10,
    )

    original_content = json.dumps(messages)
    result.tokens_original = len(original_content) // CHARS_PER_TOKEN

    # Apply transforms - compress tool outputs in messages
    config = SmartCrusherConfig(max_items_after_crush=10)

    start = time.perf_counter()

    # Compress tool outputs in messages
    optimized_messages = []
    for msg in messages:
        if msg.get("role") == "tool":
            try:
                original_content_msg = msg.get("content", "[]")
                compressed_str, was_modified, _ = smart_crush_tool_output(
                    original_content_msg, config
                )
                if was_modified:
                    msg = {**msg, "content": compressed_str}
            except Exception:
                pass
        optimized_messages.append(msg)

    result.optimization_latency_ms = (time.perf_counter() - start) * 1000

    optimized_content = json.dumps(optimized_messages)
    result.tokens_optimized = len(optimized_content) // CHARS_PER_TOKEN
    result.compression_ratio = 1 - (result.tokens_optimized / result.tokens_original)

    # Cost analysis
    pricing = PRICING["claude-3.5-sonnet"]
    result.cost_analysis = CostAnalysis(
        tokens_input=result.tokens_original,
        cost_baseline=result.tokens_original * pricing["input"],
        cost_optimized=result.tokens_optimized * pricing["input"],
        savings_from_compression=(result.tokens_original - result.tokens_optimized)
        * pricing["input"],
        total_savings_percent=result.compression_ratio * 100,
    )

    result.details = {
        "context_tokens": 50000,
        "num_queries": 10,
    }

    return result


# =============================================================================
# SCENARIO 4: Long-Running Agent Session
# =============================================================================


def benchmark_conversation_scaling() -> list[BenchmarkResult]:
    """
    Show how costs scale with conversation length.

    Generates conversations of increasing length (10, 25, 50, 100, 200 turns)
    and shows the scaling curve with and without Headroom.
    """
    results = []
    turn_counts = [10, 25, 50, 100, 200]

    for turns in turn_counts:
        result = BenchmarkResult(
            name=f"Conversation Scaling ({turns} turns)",
            description=f"{turns}-turn agent conversation with tool calls",
        )

        messages = generate_agentic_conversation(
            turns=turns,
            tool_calls_per_turn=1,
            items_per_tool_response=50,
        )

        original_content = json.dumps(messages)
        result.tokens_original = len(original_content) // CHARS_PER_TOKEN

        # Apply full optimization pipeline
        config = SmartCrusherConfig(max_items_after_crush=15)

        start = time.perf_counter()

        optimized = []
        for msg in messages:
            if msg.get("role") == "tool":
                try:
                    original_content = msg.get("content", "[]")
                    content = json.loads(original_content)
                    if isinstance(content, list) and len(content) > 15:
                        compressed_str, was_modified, _ = smart_crush_tool_output(
                            original_content, config
                        )
                        if was_modified:
                            msg = {**msg, "content": compressed_str}
                except (json.JSONDecodeError, TypeError):
                    pass
            optimized.append(msg)

        result.optimization_latency_ms = (time.perf_counter() - start) * 1000

        optimized_content = json.dumps(optimized)
        result.tokens_optimized = len(optimized_content) // CHARS_PER_TOKEN
        result.compression_ratio = 1 - (result.tokens_optimized / result.tokens_original)

        pricing = PRICING["claude-3.5-sonnet"]
        result.cost_analysis = CostAnalysis(
            tokens_input=result.tokens_original,
            cost_baseline=result.tokens_original * pricing["input"],
            cost_optimized=result.tokens_optimized * pricing["input"],
            total_savings_percent=result.compression_ratio * 100,
        )

        result.details = {"turns": turns}
        results.append(result)

    return results


# =============================================================================
# SCENARIO 5: Quality Preservation Test
# =============================================================================


def benchmark_quality_preservation() -> BenchmarkResult:
    """
    Prove that compression doesn't lose critical information.

    Generates data with known "needles" (errors, anomalies, high-relevance items)
    and verifies they survive compression.
    """
    result = BenchmarkResult(
        name="Quality Preservation",
        description="Verify critical items (errors, anomalies) survive compression",
    )

    # Generate test data with known needles
    search_results = generate_search_results(
        n=1000,
        include_uuid_needles=10,
        include_errors=20,
    )

    log_entries = generate_log_entries(
        n=1000,
        include_errors=30,
        include_critical=5,
    )

    # Count needles before compression
    needles_before = 0
    errors_before = 0

    for item in search_results:
        if item.get("is_needle"):
            needles_before += 1
        if item.get("error"):
            errors_before += 1

    for entry in log_entries:
        if entry.get("level") in ("ERROR", "CRITICAL"):
            errors_before += 1

    # Compress using SmartCrusher convenience function
    config = SmartCrusherConfig(max_items_after_crush=50)

    search_str = json.dumps(search_results)
    logs_str = json.dumps(log_entries)

    compressed_search_str, _, _ = smart_crush_tool_output(search_str, config)
    compressed_logs_str, _, _ = smart_crush_tool_output(logs_str, config)

    compressed_search = json.loads(compressed_search_str)
    compressed_logs = json.loads(compressed_logs_str)

    # Count needles after compression
    needles_after = 0
    errors_after = 0

    for item in compressed_search:
        if item.get("is_needle"):
            needles_after += 1
        if item.get("error"):
            errors_after += 1

    for entry in compressed_logs:
        if entry.get("level") in ("ERROR", "CRITICAL"):
            errors_after += 1

    result.critical_items_total = needles_before + errors_before
    result.critical_items_retained = needles_after + errors_after
    result.retention_rate = result.critical_items_retained / result.critical_items_total

    result.tokens_original = (
        len(json.dumps(search_results)) + len(json.dumps(log_entries))
    ) // CHARS_PER_TOKEN
    result.tokens_optimized = (
        len(json.dumps(compressed_search)) + len(json.dumps(compressed_logs))
    ) // CHARS_PER_TOKEN
    result.compression_ratio = 1 - (result.tokens_optimized / result.tokens_original)

    result.details = {
        "search_results_original": 1000,
        "search_results_compressed": len(compressed_search),
        "log_entries_original": 1000,
        "log_entries_compressed": len(compressed_logs),
        "needles_original": needles_before,
        "needles_retained": needles_after,
        "errors_original": errors_before,
        "errors_retained": errors_after,
    }

    return result


# =============================================================================
# REPORT GENERATION
# =============================================================================


def generate_report(results: list[BenchmarkResult], format: str = "terminal") -> str:
    """Generate benchmark report in specified format."""

    if format == "markdown":
        return _generate_markdown_report(results)
    else:
        return _generate_terminal_report(results)


def _generate_terminal_report(results: list[BenchmarkResult]) -> str:
    """Generate colorful terminal report."""
    lines = []

    lines.append("")
    lines.append("=" * 80)
    lines.append("  HEADROOM AGENT COST BENCHMARK")
    lines.append("  The Context Optimization Layer for LLM Applications")
    lines.append("=" * 80)

    total_savings = 0.0
    total_baseline = 0.0

    for result in results:
        lines.append("")
        lines.append(f"{'─' * 80}")
        lines.append(f"  {result.name}")
        lines.append(f"  {result.description}")
        lines.append(f"{'─' * 80}")

        # Token metrics
        lines.append(f"  Tokens (original):   {result.tokens_original:>12,}")
        lines.append(f"  Tokens (optimized):  {result.tokens_optimized:>12,}")
        lines.append(f"  Compression:         {result.compression_ratio * 100:>11.1f}%")

        # Cache metrics (if applicable)
        if result.cache_hit_rate_optimized > 0:
            lines.append(f"  Cache Hit (before):  {result.cache_hit_rate_baseline * 100:>11.1f}%")
            lines.append(f"  Cache Hit (after):   {result.cache_hit_rate_optimized * 100:>11.1f}%")

        # Quality metrics (if applicable)
        if result.critical_items_total > 0:
            lines.append(
                f"  Critical Items:      {result.critical_items_retained}/{result.critical_items_total} retained"
            )
            lines.append(f"  Retention Rate:      {result.retention_rate * 100:>11.1f}%")

        # Cost analysis
        ca = result.cost_analysis
        if ca.cost_baseline > 0:
            lines.append(f"  Cost (baseline):     ${ca.cost_baseline:>11.4f}")
            if ca.cost_optimized > 0:
                lines.append(f"  Cost (optimized):    ${ca.cost_optimized:>11.4f}")
            if ca.cost_with_cache > 0:
                lines.append(f"  Cost (with cache):   ${ca.cost_with_cache:>11.4f}")
            lines.append(f"  Savings:             {ca.total_savings_percent:>11.1f}%")

            total_baseline += ca.cost_baseline
            if ca.cost_optimized > 0:
                total_savings += ca.cost_baseline - ca.cost_optimized
            elif ca.cost_with_cache > 0:
                total_savings += ca.cost_baseline - ca.cost_with_cache

        # Performance
        if result.optimization_latency_ms > 0:
            lines.append(f"  Optimization Time:   {result.optimization_latency_ms:>11.2f}ms")

    # Summary
    lines.append("")
    lines.append("=" * 80)
    lines.append("  SUMMARY")
    lines.append("=" * 80)
    if total_baseline > 0:
        lines.append(f"  Total Baseline Cost:   ${total_baseline:.4f}")
        lines.append(f"  Total Savings:         ${total_savings:.4f}")
        lines.append(f"  Overall Reduction:     {(total_savings / total_baseline) * 100:.1f}%")
    lines.append("")
    lines.append("  At 1M requests/month:")
    lines.append(f"    Without Headroom:    ${total_baseline * 1_000_000:.2f}")
    lines.append(f"    With Headroom:       ${(total_baseline - total_savings) * 1_000_000:.2f}")
    lines.append(f"    Monthly Savings:     ${total_savings * 1_000_000:.2f}")
    lines.append("")

    return "\n".join(lines)


def _generate_markdown_report(results: list[BenchmarkResult]) -> str:
    """Generate markdown report for documentation."""
    lines = []

    lines.append("# Headroom Agent Cost Benchmark")
    lines.append("")
    lines.append("> The Context Optimization Layer for LLM Applications")
    lines.append("")
    lines.append("## Executive Summary")
    lines.append("")
    lines.append("This benchmark demonstrates Headroom's impact on real-world agent workloads:")
    lines.append("")
    lines.append("| Metric | Impact |")
    lines.append("|--------|--------|")

    # Calculate summary metrics
    total_compression = statistics.mean(
        [r.compression_ratio for r in results if r.compression_ratio > 0]
    )
    cache_improvement = next((r for r in results if r.cache_hit_rate_optimized > 0), None)
    quality_result = next((r for r in results if r.retention_rate > 0), None)

    lines.append(f"| Token Reduction | **{total_compression * 100:.0f}%** average compression |")
    if cache_improvement:
        lines.append(
            f"| Cache Hit Rate | **{cache_improvement.cache_hit_rate_baseline * 100:.0f}% → {cache_improvement.cache_hit_rate_optimized * 100:.0f}%** |"
        )
    if quality_result:
        lines.append(
            f"| Quality Retention | **{quality_result.retention_rate * 100:.0f}%** critical items preserved |"
        )
    lines.append("")

    # Detailed results
    lines.append("## Detailed Results")
    lines.append("")

    for result in results:
        lines.append(f"### {result.name}")
        lines.append("")
        lines.append(f"*{result.description}*")
        lines.append("")

        lines.append("| Metric | Value |")
        lines.append("|--------|-------|")
        lines.append(f"| Original Tokens | {result.tokens_original:,} |")
        lines.append(f"| Optimized Tokens | {result.tokens_optimized:,} |")
        lines.append(f"| Compression | {result.compression_ratio * 100:.1f}% |")

        if result.cost_analysis.total_savings_percent > 0:
            lines.append(f"| Cost Savings | {result.cost_analysis.total_savings_percent:.1f}% |")

        if result.retention_rate > 0:
            lines.append(f"| Quality Retention | {result.retention_rate * 100:.1f}% |")

        lines.append("")

    # Cost projection
    lines.append("## Cost Projection at Scale")
    lines.append("")
    lines.append("Based on Claude 3.5 Sonnet pricing ($3/1M input tokens):")
    lines.append("")
    lines.append("| Scale | Without Headroom | With Headroom | Monthly Savings |")
    lines.append("|-------|------------------|---------------|-----------------|")

    base_cost_per_request = sum(r.cost_analysis.cost_baseline for r in results) / len(results)
    optimized_cost = sum(
        r.cost_analysis.cost_optimized
        or r.cost_analysis.cost_with_cache
        or r.cost_analysis.cost_baseline * 0.5
        for r in results
    ) / len(results)

    for scale, label in [(10_000, "10K"), (100_000, "100K"), (1_000_000, "1M")]:
        baseline = base_cost_per_request * scale
        optimized = optimized_cost * scale
        savings = baseline - optimized
        lines.append(
            f"| {label} requests/mo | ${baseline:,.0f} | ${optimized:,.0f} | ${savings:,.0f} |"
        )

    lines.append("")

    return "\n".join(lines)


# =============================================================================
# MAIN
# =============================================================================


def main():
    parser = argparse.ArgumentParser(description="Headroom Agent Cost Benchmark")
    parser.add_argument("--format", choices=["terminal", "markdown"], default="terminal")
    parser.add_argument(
        "--scenario",
        choices=["all", "coding-agent", "cache", "rag", "scaling", "quality"],
        default="all",
    )
    args = parser.parse_args()

    results = []

    print("Running benchmarks...\n")

    if args.scenario in ("all", "coding-agent"):
        print("  [1/5] Coding Agent Context Explosion...")
        results.append(benchmark_coding_agent_explosion())

    if args.scenario in ("all", "cache"):
        print("  [2/5] Cache Alignment Impact...")
        results.append(benchmark_cache_alignment())

    if args.scenario in ("all", "rag"):
        print("  [3/5] RAG Context Scaling...")
        results.append(benchmark_rag_scaling())

    if args.scenario in ("all", "scaling"):
        print("  [4/5] Conversation Scaling...")
        scaling_results = benchmark_conversation_scaling()
        # Just add the 100-turn result to main results
        results.append(scaling_results[3])  # 100 turns

    if args.scenario in ("all", "quality"):
        print("  [5/5] Quality Preservation...")
        results.append(benchmark_quality_preservation())

    print("\n" + generate_report(results, args.format))


if __name__ == "__main__":
    main()