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Benchmarks:
A. Coding Agent Tasks
B. Research Agent Tasks
C. Tool-Use Tasks
D. Document / Contract / QA Tasks
E. Long-Horizon Agent Tasks
Baselines:
A. always frontier model
B. always cheap model
C. static model routing
D. prompt-only router
E. rules-only optimizer
F. learned model router
G. learned router + context budgeter
H. learned router + context + verifier budgeter
I. full Agent Cost Optimizer
Metrics:
- task success
- cost per successful task
- cost reduction at iso-quality
- latency
- token usage
- model calls
- tool calls
- verifier calls
- retries
- cache hit rate
- context tokens
- false-DONE rate
- unsafe cheap-model miss rate
- missed escalation rate
- user correction rate
- regression rate
- quality/cost frontier
"""
import json
import time
from typing import Dict, List, Any, Optional
from dataclasses import dataclass, field
from collections import defaultdict
from aco.optimizer import AgentCostOptimizer, OptimizationResult
from aco.config import ACOConfig, ModelConfig, ToolConfig, VerifierConfig, RoutingPolicy
from aco.trace_schema import AgentTrace, TraceStep, ModelCall, ToolCall, VerifierCall, TaskType, Outcome, FailureTag
from aco.datasets.synthetic_traces import SyntheticTraceGenerator
@dataclass
class BenchmarkConfig:
name: str
task_types: List[TaskType]
num_tasks: int
routing_mode: str = "cascade"
enable_modules: Dict[str, bool] = field(default_factory=dict)
baseline_name: str = ""
@dataclass
class BenchmarkResult:
benchmark_name: str
baseline_name: str
num_tasks: int
num_success: int
num_partial: int
num_failure: int
num_false_done: int
num_blocked: int
total_cost: float
avg_cost_success: float
avg_latency_ms: float
total_tool_calls: int
total_verifier_calls: int
total_retries: int
avg_cache_hit_rate: float
total_context_tokens: int
cost_reduction_vs_frontier: float
false_done_rate: float
unsafe_cheap_miss_rate: float
missed_escalation_rate: float
regression_rate: float
quality_cost_frontier: List[Dict[str, float]] = field(default_factory=list)
per_task_results: List[Dict[str, Any]] = field(default_factory=list)
class BenchmarkSuite:
"""Runs ACO benchmarks across tasks and baselines."""
def __init__(self, config: Optional[ACOConfig] = None):
self.config = config or self._default_config()
def _default_config(self) -> ACOConfig:
models = {
"tiny_local": ModelConfig("tiny_local", "local", 0.0001, 0.0002, latency_ms_estimate=200, strength_tier=1),
"cheap_cloud": ModelConfig("cheap_cloud", "cloud", 0.0005, 0.001, latency_ms_estimate=500, strength_tier=2),
"medium": ModelConfig("medium", "cloud", 0.003, 0.006, latency_ms_estimate=800, strength_tier=3),
"frontier": ModelConfig("frontier", "cloud", 0.01, 0.03, latency_ms_estimate=1500, strength_tier=4),
"specialist": ModelConfig("specialist", "cloud", 0.015, 0.045, latency_ms_estimate=2000, strength_tier=5),
}
tools = {
"search": ToolConfig("search", 0.002, 500),
"retrieve": ToolConfig("retrieve", 0.001, 300),
"code_execution": ToolConfig("code_execution", 0.005, 1000),
"linter": ToolConfig("linter", 0.001, 200),
"file_read": ToolConfig("file_read", 0.0005, 100),
"compliance_check": ToolConfig("compliance_check", 0.01, 1500),
"summarize": ToolConfig("summarize", 0.002, 400),
}
verifiers = {
"verifier_medium": VerifierConfig("verifier_medium", 0.005, 800, 0.8),
}
return ACOConfig(
project_name="aco-benchmark",
models=models,
tools=tools,
verifiers=verifiers,
routing_policy=RoutingPolicy("benchmark"),
)
def generate_benchmark_data(self, n: int = 1000, seed: int = 42) -> List[AgentTrace]:
"""Generate synthetic traces for benchmarking."""
gen = SyntheticTraceGenerator(seed=seed)
return gen.generate(n)
def run_baseline(
self,
traces: List[AgentTrace],
baseline_name: str,
) -> BenchmarkResult:
"""Run a single baseline over the benchmark traces."""
# Configure optimizer for baseline
mode_map = {
"always_frontier": "always_frontier",
"always_cheap": "always_frontier", # overridden below
"static": "static",
"prompt_only": "prompt_only",
"learned": "learned",
"learned_verifier": "learned_verifier",
"cascade": "cascade",
"rules_only": "cascade", # uses cascade routing with rules-based modules
"full": "cascade",
}
# Adjust config based on baseline
config = self._default_config()
if baseline_name == "always_frontier":
config.enable_router = False
elif baseline_name == "always_cheap":
config.enable_router = False
# Override all models to cheap tier in simulation by using special handling
elif baseline_name == "static":
pass # default static routing
elif baseline_name == "prompt_only":
pass # prompt heuristic routing
elif baseline_name == "rules_only":
config.enable_classifier = True
config.enable_router = True
config.enable_context_budgeter = True
config.enable_cache_layout = True
config.enable_tool_gate = True
config.enable_verifier_budgeter = True
config.enable_retry_optimizer = True
config.enable_meta_tool_miner = False
config.enable_early_termination = True
elif baseline_name == "full":
pass # all enabled
# For ablations, disable specific modules
if baseline_name.startswith("no_"):
module_name = baseline_name.replace("no_", "")
if hasattr(config, f"enable_{module_name}"):
setattr(config, f"enable_{module_name}", False)
optimizer = AgentCostOptimizer(config)
results = []
total_cost = 0.0
total_latency = 0.0
total_tools = 0
total_verifiers = 0
total_retries = 0
total_context = 0
cache_rates = []
success_count = 0
partial_count = 0
failure_count = 0
false_done_count = 0
blocked_count = 0
cheap_misses = 0
escalation_misses = 0
regression_count = 0
frontier_costs = []
actual_costs = []
for trace in traces:
# Run optimization on this trace's request
run_state = {
"trace_id": trace.trace_id,
"routing_mode": mode_map.get(baseline_name, "cascade"),
"current_cost": 0.0,
"planned_tools": [
(tc.tool_name, tc.tool_input)
for step in trace.steps
for tc in step.tool_calls
],
"previous_tool_calls": [
tc for step in trace.steps for tc in step.tool_calls
],
"step_number": len(trace.steps),
"total_steps": len(trace.steps),
"is_irreversible": trace.task_type == TaskType.LEGAL_REGULATED,
}
result = optimizer.optimize(trace.user_request, run_state)
# Simulate execution based on optimization decisions
sim_cost, sim_latency, sim_success = self._simulate(trace, result, baseline_name)
total_cost += sim_cost
total_latency += sim_latency
total_tools += len(result.tool_decisions)
if result.verifier_decision:
total_verifiers += 1
total_retries += sum(1 for d in result.tool_decisions if d.decision.value == "skip")
total_context += sum(s.context_size_tokens for s in trace.steps)
frontier_cost = sum(
s.model_call.total_cost if s.model_call else 0
for s in trace.steps
) if trace.metadata.get("scenario") == "frontier_unnecessary" else trace.total_cost * 2
frontier_costs.append(frontier_cost)
actual_costs.append(sim_cost)
outcome = trace.final_outcome
if sim_success:
if outcome == Outcome.SUCCESS:
success_count += 1
elif outcome == Outcome.PARTIAL_SUCCESS:
partial_count += 1
else:
regression_count += 1
else:
if outcome == Outcome.FALSE_DONE:
false_done_count += 1
elif outcome == Outcome.BLOCKED:
blocked_count += 1
else:
failure_count += 1
# Check for cheap model misses
if trace.metadata.get("scenario") == "cheap_failure" and result.routing_decision.tier <= 2:
cheap_misses += 1
# Check for missed escalation
if trace.metadata.get("scenario") in ("cheap_failure", "tool_underuse") and result.routing_decision.tier < 3:
escalation_misses += 1
cache_rates.append(trace.cache_hit_rate)
results.append({
"trace_id": trace.trace_id,
"task_type": trace.task_type.value,
"scenario": trace.metadata.get("scenario", "normal"),
"simulated_cost": sim_cost,
"simulated_success": sim_success,
"routing_tier": result.routing_decision.tier,
"model_id": result.routing_decision.model_id,
"tool_count": len(result.tool_decisions),
"verifier_used": result.verifier_decision is not None,
})
n = len(traces)
avg_cost_success = total_cost / max(success_count + partial_count, 1)
# Cost reduction vs frontier baseline
cost_reduction = (sum(frontier_costs) - sum(actual_costs)) / max(sum(frontier_costs), 1)
return BenchmarkResult(
benchmark_name="synthetic_benchmark",
baseline_name=baseline_name,
num_tasks=n,
num_success=success_count,
num_partial=partial_count,
num_failure=failure_count,
num_false_done=false_done_count,
num_blocked=blocked_count,
total_cost=total_cost,
avg_cost_success=avg_cost_success,
avg_latency_ms=total_latency / n,
total_tool_calls=total_tools,
total_verifier_calls=total_verifiers,
total_retries=total_retries,
avg_cache_hit_rate=sum(cache_rates) / n,
total_context_tokens=total_context,
cost_reduction_vs_frontier=cost_reduction,
false_done_rate=false_done_count / n,
unsafe_cheap_miss_rate=cheap_misses / n,
missed_escalation_rate=escalation_misses / n,
regression_rate=regression_count / n,
quality_cost_frontier=[
{"cost": c, "success": 1.0 if s else 0.0}
for c, s in zip(actual_costs, [r["simulated_success"] for r in results])
],
per_task_results=results,
)
def _simulate(self, trace: AgentTrace, result: OptimizationResult, baseline: str) -> tuple:
"""Simulate execution based on optimizer decisions."""
# Base cost from the trace
base_cost = trace.total_cost_computed
# Adjust cost based on routing decision
tier = result.routing_decision.tier
cost_mult = {
1: 0.05, 2: 0.25, 3: 0.75, 4: 1.0, 5: 1.5,
}.get(tier, 1.0)
# Override for always_cheap baseline
if baseline == "always_cheap":
cost_mult = 0.25
tier = 2
# Override for always_frontier baseline
if baseline == "always_frontier":
cost_mult = 1.0
tier = 4
# Apply tool gate savings
tools_skipped = sum(1 for d in result.tool_decisions if d.decision.value in ("skip", "use_cache"))
tool_savings = tools_skipped * 0.005
# Apply cache savings
cache_savings = 0.0
if result.prompt_layout:
cache_savings = result.prompt_layout.cache_discount
sim_cost = base_cost * cost_mult - tool_savings - cache_savings
sim_cost = max(sim_cost, 0.001)
# Simulate latency
sim_latency = trace.total_latency_ms * cost_mult * 0.8
# Simulate success probability
scenario = trace.metadata.get("scenario", "normal")
# Base success rate by tier and scenario
success_prob = 0.95 if tier >= 3 else 0.7
if scenario == "cheap_failure":
success_prob = 0.3 if tier <= 2 else 0.85
elif scenario == "tool_underuse":
success_prob = 0.6 if tools_skipped > 0 else 0.8
elif scenario == "retry_loop":
success_prob = 0.2
elif scenario == "frontier_unnecessary":
success_prob = 0.95
elif scenario == "meta_tool_success":
success_prob = 0.9
elif scenario == "meta_tool_bad":
success_prob = 0.4
elif scenario == "false_done":
success_prob = 0.1
elif scenario == "blocked" or scenario == "stopped_doom":
success_prob = 0.0
elif scenario == "human_escalation":
success_prob = 0.5
# Verifier improves success for high-risk tasks
if result.verifier_decision and result.verifier_decision.decision.value == "call_verifier":
success_prob += 0.05
# Meta-tool success bonus
if result.meta_tool_match:
success_prob += 0.03
sim_success = success_prob > 0.5 # simplified threshold
return sim_cost, sim_latency, sim_success
def run_all_baselines(self, traces: List[AgentTrace]) -> Dict[str, BenchmarkResult]:
"""Run all baseline configurations."""
baselines = [
"always_frontier",
"always_cheap",
"static",
"prompt_only",
"cascade",
"rules_only",
"full",
]
results = {}
for baseline in baselines:
print(f"Running baseline: {baseline}...")
results[baseline] = self.run_baseline(traces, baseline)
return results
def run_ablations(self, traces: List[AgentTrace]) -> Dict[str, BenchmarkResult]:
"""Run ablation study disabling each module."""
ablations = [
"no_router",
"no_context_budgeter",
"no_cache_layout",
"no_tool_gate",
"no_verifier_budgeter",
"no_retry_optimizer",
"no_meta_tool_miner",
"no_early_termination",
]
results = {}
for ablation in ablations:
print(f"Running ablation: {ablation}...")
results[ablation] = self.run_baseline(traces, ablation)
return results
def report(self, results: Dict[str, BenchmarkResult]) -> str:
"""Generate formatted benchmark report."""
lines = ["=" * 80, "AGENT COST OPTIMIZER BENCHMARK REPORT", "=" * 80, ""]
headers = ["Baseline", "Success", "Partial", "Fail", "Blocked", "False-DONE",
"Total Cost", "Avg Cost/Succ", "Latency(ms)", "Tools", "Verifiers",
"Retries", "Cache Hit", "Cost Reduction", "Regression"]
lines.append(" | ".join(headers))
lines.append("-" * 120)
for name, result in results.items():
row = [
name[:20].ljust(20),
f"{result.num_success / result.num_tasks:.1%}",
f"{result.num_partial / result.num_tasks:.1%}",
f"{result.num_failure / result.num_tasks:.1%}",
f"{result.num_blocked / result.num_tasks:.1%}",
f"{result.false_done_rate:.1%}",
f"${result.total_cost:.2f}",
f"${result.avg_cost_success:.4f}",
f"{result.avg_latency_ms:.0f}",
str(result.total_tool_calls),
str(result.total_verifier_calls),
str(result.total_retries),
f"{result.avg_cache_hit_rate:.1%}",
f"{result.cost_reduction_vs_frontier:.1%}",
f"{result.regression_rate:.1%}",
]
lines.append(" | ".join(row))
lines.append("")
lines.append("=" * 80)
# Find best cost/success tradeoff
best_score = -float("inf")
best_name = ""
for name, result in results.items():
success_rate = (result.num_success + result.num_partial) / result.num_tasks
score = success_rate * 10 - result.avg_cost_success * 100 - result.regression_rate * 50
if score > best_score:
best_score = score
best_name = name
lines.append(f"BEST OVERALL: {best_name} (score={best_score:.2f})")
lines.append("")
return "\n".join(lines)
def export(self, results: Dict[str, BenchmarkResult], path: str) -> None:
"""Export results to JSON."""
export_data = {}
for name, result in results.items():
export_data[name] = {
"benchmark_name": result.benchmark_name,
"baseline_name": result.baseline_name,
"num_tasks": result.num_tasks,
"num_success": result.num_success,
"num_partial": result.num_partial,
"num_failure": result.num_failure,
"num_false_done": result.num_false_done,
"num_blocked": result.num_blocked,
"total_cost": result.total_cost,
"avg_cost_success": result.avg_cost_success,
"avg_latency_ms": result.avg_latency_ms,
"total_tool_calls": result.total_tool_calls,
"total_verifier_calls": result.total_verifier_calls,
"total_retries": result.total_retries,
"avg_cache_hit_rate": result.avg_cache_hit_rate,
"total_context_tokens": result.total_context_tokens,
"cost_reduction_vs_frontier": result.cost_reduction_vs_frontier,
"false_done_rate": result.false_done_rate,
"unsafe_cheap_miss_rate": result.unsafe_cheap_miss_rate,
"missed_escalation_rate": result.missed_escalation_rate,
"regression_rate": result.regression_rate,
}
with open(path, "w") as f:
json.dump(export_data, f, indent=2)
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