multiAI Summary Pending
Agent Observability & Monitoring
Score, monitor, and troubleshoot AI agent fleets in production. Built for ops teams running 1-100+ agents.
3,556 stars
byopenclaw
Installation
Claude Code / Cursor / Codex
$curl -o ~/.claude/skills/afrexai-agent-observability/SKILL.md --create-dirs "https://raw.githubusercontent.com/openclaw/skills/main/skills/1kalin/afrexai-agent-observability/SKILL.md"
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/afrexai-agent-observability/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How Agent Observability & Monitoring Compares
| Feature / Agent | Agent Observability & Monitoring | Standard Approach |
|---|---|---|
| Platform Support | multi | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Score, monitor, and troubleshoot AI agent fleets in production. Built for ops teams running 1-100+ agents.
Which AI agents support this skill?
This skill is compatible with multi.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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: ``` Run the agent observability assessment against our current deployment: - How many agents are running? - What monitoring exists today? - What broke in the last 30 days? - What's our monthly agent spend? - Who gets alerted when an agent fails? ``` ## Cost Framework | Company Size | Unmonitored Waste | Monitoring Investment | Net Savings | |-------------|-------------------|----------------------|-------------| | 1-5 agents | $2K-$8K/mo | $500-$1K/mo | $1.5K-$7K/mo | | 5-20 agents | $8K-$45K/mo | $2K-$5K/mo | $6K-$40K/mo | | 20-100 agents | $45K-$200K/mo | $8K-$20K/mo | $37K-$180K/mo | ## 90-Day Monitoring Roadmap **Week 1-2**: Inventory all agents, document intended scope, tag cost centers **Week 3-4**: Deploy execution logging (every tool call, every output) **Month 2**: Build dashboards — cost per task, error rate, latency P95 **Month 3**: Automated alerting — failure detection <5 min, cost anomaly flags, scope violations ## 7 Monitoring Mistakes 1. Logging only errors (miss the slow degradation) 2. No cost attribution (agents burn budget invisibly) 3. Monitoring agents like servers (they need task-level observability) 4. Manual review of agent outputs (doesn't scale past 3 agents) 5. No baseline metrics (can't detect regression without a baseline) 6. Alerting on everything (alert fatigue kills response time) 7. Skipping agent-to-agent handoff monitoring (where most fleet failures happen) ## Industry Adjustments | Industry | Critical Dimension | Why | |----------|-------------------|-----| | Financial Services | Security & Boundaries | Regulatory audit trails mandatory | | Healthcare | Output Quality | Clinical accuracy non-negotiable | | Legal | Execution Visibility | Billing requires task-level tracking | | Ecommerce | Cost Attribution | Margin-sensitive, waste kills profit | | SaaS | Fleet Coordination | Multi-tenant agent isolation | | Manufacturing | Failure Recovery | Downtime = production line stops | | Construction | Security & Boundaries | Safety-critical document handling | | Real Estate | Output Quality | Valuation errors = liability | | Recruitment | Fleet Coordination | Candidate pipeline handoffs | | Professional Services | Cost Attribution | Client billing accuracy | --- ## Go Deeper - **AI Agent Context Packs** — industry-specific decision frameworks: https://afrexai-cto.github.io/context-packs/ - **AI Revenue Leak Calculator** — find where your business loses money to manual processes: https://afrexai-cto.github.io/ai-revenue-calculator/ - **Agent Setup Wizard** — configure your agent stack in 5 minutes: https://afrexai-cto.github.io/agent-setup/ Built by AfrexAI — we help businesses run AI agents that actually make money.