Rechercher

PythonReactSEOPDF
OpenClaw

Afrexai Agent Observability

Agent Observability & Monitoring

1kalin0 installations selon la source☆ 0 Étoiles du dépôt

Installation

Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.

SkillHub CLI

skillhub install 1kalin/afrexai-agent-observability

Examinez la source et la compatibilité, puis suivez les étapes d’installation.

Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.

Voir la source ↗Guides

Instructions

Agent Observability & Monitoring

Score, monitor, and troubleshoot AI agent fleets in production. Built for ops teams running 1-100+ agents.

What This Does

Evaluates your agent deployment across 6 dimensions and returns a 0-100 health score with specific fixes.

6-Dimension Assessment

1. Execution Visibility (0-20 pts)
  • Can you see what every agent is doing right now?
  • Task queue depth, active/idle ratio, error rates
  • Benchmark: Top quartile tracks 95%+ of agent actions in real-time
2. Cost Attribution (0-20 pts)
  • Do you know exactly what each agent costs per task?
  • Token spend, API calls, compute time, tool invocations
  • Benchmark: Unmonitored agents waste 30-55% on retries and hallucination loops
3. Output Quality (0-15 pts)
  • Are agent outputs validated before reaching users or systems?
  • Accuracy sampling, hallucination detection, regression tracking
  • Benchmark: 1 in 12 agent outputs contains a material error without monitoring
4. Failure Recovery (0-15 pts)
  • What happens when an agent fails mid-task?
  • Retry logic, graceful degradation, human escalation paths
  • Benchmark: Mean time to detect agent failure without monitoring: 4.2 hours
5. Security & Boundaries (0-15 pts)
  • Are agents staying within authorized scope?
  • Tool access auditing, data exfiltration checks, permission drift
  • Benchmark: 23% of production agents access tools outside their intended scope
6. Fleet Coordination (0-15 pts)
  • Do multi-agent workflows hand off cleanly?
  • Message passing reliability, deadlock detection, duplicate work
  • Benchmark: Uncoordinated fleets duplicate 18-25% of work

Scoring

Score Rating Action
80-100 Production-grade Optimize and scale
60-79 Operational Fix gaps before scaling
40-59 Risky Immediate remediation needed
0-39 Blind Stop scaling, instrument first

Quick Assessment Prompt

Ask the agent to evaluate your setup:

``...

Informations sur la source

Source
github
Repository
openclaw/skills
Path
skills/1kalin/afrexai-agent-observability
Version
—
Commit
d9150b35c79ef56829463fe296d59e8b80a16a72
Source de classification
rules
Statut
active
Visibilité
public

Ces informations proviennent de l’analyse indexée et peuvent être anciennes. Vérifiez les fichiers actuels.

README

Agent Observability & Monitoring

Score your AI agent fleet health across 6 dimensions (0-100). Detect waste, prevent failures, attribute costs.

What you get:

  • 6-dimension health scoring (execution visibility, cost attribution, output quality, failure recovery, security, fleet coordination)
  • 2026 benchmarks per dimension
  • Cost framework by fleet size (1-100+ agents)
  • 90-day monitoring roadmap
  • 7 common monitoring mistakes
  • Industry-specific priority adjustments (10 verticals)

Who it's for: Ops teams, platform engineers, CTOs running AI agents in production.

Links:

Built by AfrexAI.

Contenu extrait

SKILL.md
# Agent Observability & Monitoring

Score, monitor, and troubleshoot AI agent fleets in production. Built for ops teams running 1-100+ agents.

## What This Does

Evaluates your agent deployment across 6 dimensions and returns a 0-100 health score with specific fixes.

## 6-Dimension Assessment

### 1. Execution Visibility (0-20 pts)
- Can you see what every agent is doing right now?
- Task queue depth, active/idle ratio, error rates
- **Benchmark**: Top quartile tracks 95%+ of agent actions in real-time

### 2. Cost Attribution (0-20 pts)
- Do you know exactly what each agent costs per task?
- Token spend, API calls, compute time, tool invocations
- **Benchmark**: Unmonitored agents waste 30-55% on retries and hallucination loops

### 3. Output Quality (0-15 pts)
- Are agent outputs validated before reaching users or systems?
- Accuracy sampling, hallucination detection, regression tracking
- **Benchmark**: 1 in 12 agent outputs contains a material error without monitoring

### 4. Failure Recovery (0-15 pts)
- What happens when an agent fails mid-task?
- Retry logic, graceful degradation, human escalation paths
- **Benchmark**: Mean time to detect agent failure without monitoring: 4.2 hours

### 5. Security & Boundaries (0-15 pts)
- Are agents staying within authorized scope?
- Tool access auditing, data exfiltration checks, permission drift
- **Benchmark**: 23% of production agents access tools outside their intended scope

### 6. Fleet Coordination (0-15 pts)
- Do multi-agent workflows hand off cleanly?
- Message passing reliability, deadlock detection, duplicate work
- **Benchmark**: Uncoordinated fleets duplicate 18-25% of work

## Scoring

| Score | Rating | Action |
|-------|--------|--------|
| 80-100 | Production-grade | Optimize and scale |
| 60-79 | Operational | Fix gaps before scaling |
| 40-59 | Risky | Immediate remediation needed |
| 0-39 | Blind | Stop scaling, instrument first |

## Quick Assessment Prompt

Ask the agent to evaluate your setup:

``...
README
# Agent Observability & Monitoring

Score your AI agent fleet health across 6 dimensions (0-100). Detect waste, prevent failures, attribute costs.

**What you get:**
- 6-dimension health scoring (execution visibility, cost attribution, output quality, failure recovery, security, fleet coordination)
- 2026 benchmarks per dimension
- Cost framework by fleet size (1-100+ agents)
- 90-day monitoring roadmap
- 7 common monitoring mistakes
- Industry-specific priority adjustments (10 verticals)

**Who it's for:** Ops teams, platform engineers, CTOs running AI agents in production.

**Links:**
- [AI Agent Context Packs](https://afrexai-cto.github.io/context-packs/) — $47 industry-specific decision frameworks
- [AI Revenue Leak Calculator](https://afrexai-cto.github.io/ai-revenue-calculator/) — find hidden waste
- [Agent Setup Wizard](https://afrexai-cto.github.io/agent-setup/) — configure your stack

Built by [AfrexAI](https://afrexai-cto.github.io/context-packs/).

Skills associés

OpenClaw

xint

0xnyk

X Intelligence CLI — search, monitor, analyze, and engage on X/Twitter. TypeScript + Bun. AI agent skill.

OpenClaw

bambu-studio-ai

heyixuan2

Bambu Lab 3D printer control and automation. Activate when user mentions: printer status, 3D printing, slice, analyze model, generate 3D, AMS filament, print monitor, Bambu Lab, or any 3D printing task. Full pi...

OpenClaw

flightclaw

jackculpan

Track flight prices from Google Flights with this OpenClaw skill. Search routes, monitor prices, and get alerts when prices drop.

OpenClaw

openclaw-trading-assistant

openclaw-trade

openclaw trading assistant| openclaw trading skill | nof1.ai & openclaw [moltbot] collaboration | We get the best practices from alpha arena trading seasons and bring it to clawdbot All top AI agents, realtime...