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agent-observability-monitoring

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

Quellinformationen

Repository
modbender/skill-library-mcp
Letzte Quellaktivität
6. März 2026 um 18:33
Erkannte Sprache von SKILL.md
Englisch
Sterne
12
Forks
3

Installationsoptionen

Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.

Quelldateien prüfen

Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.

SKILL.md wird angezeigt

SKILL.md
Quellanweisungen · Schreibgeschützte Vorschau
name
Agent Observability & Monitoring
description
Score, monitor, and troubleshoot AI agent fleets in production. Built for ops teams running 1-100+ agents.
# 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.
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