perf-agent

name	perf-agent
description	Measure before optimizing, target actual bottlenecks proven by profiling, and verify improvements with benchmarks. Use when the app is slow, memory is leaking, queries lag, or costs need reducing.
domain	agents
tags	["agent","ai-agent","automation","orchestration","perf"]

Perf Agent

When to Use

Trigger phrases:

• "perf agent"

• "Application is slow and you need to find out why"

• "Database queries are taking too long"

• "Memory usage is growing (leak detection)"

• Application is slow and you need to find out why

• Database queries are taking too long

• Memory usage is growing (leak detection)

• API response times are degrading

• Preparing for higher traffic (capacity planning)

• Reducing cloud infrastructure costs

• Optimizing build times or CI pipeline duration

• Investigating high CPU usage

When NOT to Use

• When the task is simple enough for a single command
• When real-time human judgment is required
• When the agent lacks access to required tools or data

Overview

Perf Agent is an AI agent skill for agent orchestration. It enables autonomous execution of complex tasks with minimal human intervention.

Capabilities

• Autonomous operation — Execute multi-step perf agent workflows independently
• Context awareness — Adapt behavior based on current state and history
• Error recovery — Handle failures gracefully with retry and fallback logic
• Integration — Connect with external tools and services as needed

Workflow

# Example: Agent orchestration
from dataclasses import dataclass

@dataclass
class Task:
    name: str
    priority: int
    assigned_agent: str

def orchestrate(tasks: list[Task]) -> dict:
    results = {}
    for task in sorted(tasks, key=lambda t: t.priority):
        results[task.name] = execute(task)
    return results

1. Initialize — Set up the agent context and load required resources
2. Plan — Break down the task into executable steps
3. Execute — Run each step, monitoring for errors and adapting as needed
4. Verify — Validate results against acceptance criteria
5. Report — Summarize outcomes and suggest next steps

Configuration

• Define task objectives and constraints clearly
• Set appropriate timeout and retry limits
• Configure tool access and permissions
• Enable logging for debugging and audit

Anti-Rationalization

Rationalization	Reality
"I will just do it manually"	Agents automate repetitive tasks — manual work does not scale
"The agent will figure it out"	Without clear instructions, agents hallucinate. Give explicit context.
"One agent is enough"	Complex tasks benefit from specialized agents working in parallel

Process

1. Prepare — Gather requirements, verify prerequisites, set up environment
2. Execute — Run perf agent workflow with configured parameters
3. Verify — Validate output meets requirements, document results

Verification

• All steps executed successfully
• Results validated against acceptance criteria
• Error handling tested with edge cases
• Documentation updated with findings