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

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

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

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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.