Cutting Infrastructure Costs 10x with AI Agents — Multi-Agent Performance Optimization Through the Meta Capacity Efficiency Pattern

# Conceptual orchestrator structure example (LangGraph style)
from langgraph.graph import StateGraph, END, START
from typing import TypedDict, Literal
 
class AgentState(TypedDict):
    task: str
    findings: list[str]
    pr_draft: str | None
    route: Literal["offense", "defense"]
 
def router(state: AgentState) -> Literal["offense_agent", "defense_agent"]:
    if state["route"] == "offense":
        return "offense_agent"
    return "defense_agent"
 
def offense_agent(state: AgentState) -> AgentState:
    # Scan for optimization opportunities with SLM — cost-efficient
    opportunities = scan_for_optimization_opportunities(state["task"])
    return {**state, "findings": opportunities}
 
def defense_agent(state: AgentState) -> AgentState:
    # Detect regression → auto-generate PR
    regression = detect_performance_regression(state["task"])
    pr = generate_fix_pr(regression) if regression else None
    return {**state, "pr_draft": pr}
 
graph = StateGraph(AgentState)
graph.add_node("offense_agent", offense_agent)
graph.add_node("defense_agent", defense_agent)
 
# Conditional entry point using the START constant — a working pattern
graph.add_conditional_edges(START, router)
graph.add_edge("offense_agent", END)
graph.add_edge("defense_agent", END)

# Inference Layering routing logic example
from enum import Enum
 
class TaskComplexity(Enum):
    SIMPLE = "simple"      # Log parsing, format conversion
    MODERATE = "moderate"  # Pattern detection, summarization
    COMPLEX = "complex"    # Root cause analysis, code generation
 
def select_model(complexity: TaskComplexity) -> str:
    # Cost ratio: SIMPLE:MODERATE:COMPLEX ≈ 1:10:150 (varies over time)
    routing_table = {
        TaskComplexity.SIMPLE: "llama-3.2-3b",      # CPU processing
        TaskComplexity.MODERATE: "llama-3.1-8b",     # GPU processing
        TaskComplexity.COMPLEX: "claude-opus-4-7",   # Frontier model
    }
    return routing_table[complexity]
 
def classify_task(task_description: str) -> TaskComplexity:
    keywords_complex = ["root cause", "generate PR", "architectural decision"]
    keywords_simple = ["parse log", "format", "count", "extract field"]
 
    if any(kw in task_description.lower() for kw in keywords_complex):
        return TaskComplexity.COMPLEX
    elif any(kw in task_description.lower() for kw in keywords_simple):
        return TaskComplexity.SIMPLE
    return TaskComplexity.MODERATE
 
# ⚠️ Warning: keyword matching is prototype-level.
# In production, fine-tuning a lightweight classification model or
# replacing this with an embedding similarity approach is far more robust.

# agents/regression_detector.py
import asyncio
import aiohttp
from dataclasses import dataclass
from anthropic import AsyncAnthropic
 
@dataclass
class PerformanceSnapshot:
    service: str
    p99_latency_ms: float
    throughput_rps: float
    error_rate: float
    timestamp: str
 
async def fetch_current_metrics(
    service_name: str,
    prometheus_url: str = "http://prometheus:9090"
) -> PerformanceSnapshot:
    """Collect real-time metrics from Prometheus"""
    queries = {
        "p99_latency": f'histogram_quantile(0.99, rate(http_request_duration_seconds_bucket{{service="{service_name}"}}[5m])) * 1000',
        "throughput": f'rate(http_requests_total{{service="{service_name}"}}[5m])',
        "error_rate": f'rate(http_requests_total{{service="{service_name}",status=~"5.."}}[5m]) / rate(http_requests_total{{service="{service_name}"}}[5m])',
    }
    results = {}
    async with aiohttp.ClientSession() as session:
        for key, query in queries.items():
            async with session.get(
                f"{prometheus_url}/api/v1/query",
                params={"query": query}
            ) as resp:
                data = await resp.json()
                value = data["data"]["result"]
                results[key] = float(value[0]["value"][1]) if value else 0.0
 
    return PerformanceSnapshot(
        service=service_name,
        p99_latency_ms=results["p99_latency"],
        throughput_rps=results["throughput"],
        error_rate=results["error_rate"],
        timestamp=asyncio.get_event_loop().time().__str__(),
    )
 
async def detect_regression(
    baseline: PerformanceSnapshot,
    current: PerformanceSnapshot,
    client: AsyncAnthropic
) -> dict:
    """
    Step 1: Rule-based numerical detection (zero cost)
    Step 2: Call frontier model only after regression is confirmed (minimize cost)
    """
    regression_signals = []
    if current.p99_latency_ms > baseline.p99_latency_ms * 1.2:
        regression_signals.append(
            f"P99 latency {baseline.p99_latency_ms:.1f}ms → {current.p99_latency_ms:.1f}ms (exceeded 20%)"
        )
    if current.error_rate > baseline.error_rate + 0.01:
        regression_signals.append(
            f"Error rate {baseline.error_rate:.2%} → {current.error_rate:.2%}"
        )
 
    if not regression_signals:
        return {"regression_detected": False}
 
    # Call frontier model only after regression is confirmed
    analysis_prompt = f"""
    The following performance regressions have been detected in service '{current.service}':
    {chr(10).join(regression_signals)}
 
    Please suggest possible root causes and immediate remediation steps.
    Response format: {{"root_cause": "...", "fix_suggestion": "...", "severity": "low|medium|high"}}
    """
 
    response = await client.messages.create(
        model="claude-opus-4-7",
        max_tokens=1024,
        messages=[{"role": "user", "content": analysis_prompt}]
    )
 
    return {
        "regression_detected": True,
        "signals": regression_signals,
        "analysis": response.content[0].text
    }

# orchestrator/capacity_workflow.py
from langgraph.graph import StateGraph, END
from langgraph.checkpoint.memory import MemorySaver
from typing import TypedDict, Annotated
import operator
 
class CapacityState(TypedDict):
    service_name: str
    metrics: dict
    regression_report: str | None
    optimization_opportunities: list[str]
    generated_prs: list[str]
    errors: Annotated[list[str], operator.add]  # Errors accumulate (prevent overwriting)
 
async def metrics_collector(state: CapacityState) -> CapacityState:
    """Collect current metrics from Prometheus/CloudWatch"""
    metrics = await fetch_current_metrics(state["service_name"])
    return {**state, "metrics": metrics}
 
async def regression_detector(state: CapacityState) -> CapacityState:
    """Detect performance regressions — SLM or rule-based"""
    report = await run_regression_analysis(state["metrics"])
    return {**state, "regression_report": report}
 
async def opportunity_scanner(state: CapacityState) -> CapacityState:
    """Scan for optimization opportunities — offense agent"""
    opportunities = await scan_efficiency_opportunities(state["metrics"])
    return {**state, "optimization_opportunities": opportunities}
 
async def pr_generator(state: CapacityState) -> CapacityState:
    """Auto-generate PRs for regression fixes or optimizations"""
    prs = []
    if state["regression_report"]:
        prs.append(await generate_regression_fix_pr(state["regression_report"]))
    for opp in state["optimization_opportunities"]:
        prs.append(await generate_optimization_pr(opp))
    return {**state, "generated_prs": prs}
 
def should_generate_pr(state: CapacityState) -> str:
    has_work = state["regression_report"] or state["optimization_opportunities"]
    return "pr_generator" if has_work else END
 
# Assemble the graph
workflow = StateGraph(CapacityState)
workflow.add_node("metrics_collector", metrics_collector)
workflow.add_node("regression_detector", regression_detector)
workflow.add_node("opportunity_scanner", opportunity_scanner)
workflow.add_node("pr_generator", pr_generator)
 
workflow.set_entry_point("metrics_collector")
 
# Adding two edges from the same source node causes LangGraph to treat them as fan-out.
# regression_detector and opportunity_scanner run concurrently.
workflow.add_edge("metrics_collector", "regression_detector")
workflow.add_edge("metrics_collector", "opportunity_scanner")
 
workflow.add_conditional_edges("regression_detector", should_generate_pr)
workflow.add_conditional_edges("opportunity_scanner", should_generate_pr)
workflow.add_edge("pr_generator", END)
 
checkpointer = MemorySaver()
app = workflow.compile(checkpointer=checkpointer)