Production-Grade Observability for LLMs & AI Agents
Debug multi-step agent workflows, track tool calls, measure RAG quality, and monitor costsâall in one platform. Group spend by agent, user, tool, or any custom dimension. Built for production AI teams.
What is Production-Grade Observability?
Complete visibility into AI agents and LLM applications:
Track multi-step agent workflows (planning â tool selection â execution â synthesis)
Measure cost by agent/user/tool with infinite grouping flexibility
Debug failed tool calls with input/output traces
Monitor RAG quality (recall@k, context hit rate, citation coverage)
Get p50/p95/p99 latency for every component
See exactly where your AI agents spend time and money
Understand Your AI Usage Patterns
Get a complete view of how your organization is using AI models. Track request volumes, identify usage trends, and understand which models are most popular among your teams.
Request Volume Tracking
Monitor daily, weekly, and monthly request volumes across all models
Model Distribution Analysis
See which models are used most frequently and by which teams
Usage Trend Identification
Identify usage patterns and predict future needs
Monthly Request Volume
Total Requests
1.24M
Avg. Daily
41.3K
Cost Analysis Dashboard
Optimize Your AI Spending
Take control of your AI costs with detailed breakdowns and projections. Identify opportunities to optimize spending while maintaining performance.
Cost Trend Analysis
Track spending over time and identify cost drivers
Cost Optimization Recommendations
Get AI-powered suggestions to reduce costs without sacrificing quality
Budget Alerts & Controls
Set spending limits and receive alerts when approaching thresholds
Measure & Improve AI Performance
Track response times, success rates, and other key performance indicators. Identify bottlenecks and optimize your AI infrastructure for better results.
Response Time Monitoring
Track latency across different models and request types
User Experience Metrics
Measure user satisfaction and engagement with AI responses
Performance Optimization
Get recommendations to improve response quality and speed
Performance Dashboard
Avg. Response Time
142ms
-8.3% from last month
Success Rate
99.8%
+0.2% from last month
Usage Tracking
Monitor request volumes, token usage, and model distribution across your organization.
Cost Analytics
Track spending by model, team, and project with detailed cost breakdowns and forecasting.
Performance Metrics
Measure latency, success rates, and other key performance indicators across all models.
AI Agent Observability
See inside multi-step agent workflows. Debug tool calls. Track agent costs.
Multi-Step Workflow Tracing
Visualize agent workflows: Planning â Tool Selection â Tool Execution â Result Synthesis. See which steps fail and why.
Tool Call Debugging
Track every tool invocation: Claude Code, browser, file system, API calls. See inputs, outputs, latency, and failures.
Agent Cost Attribution
See spend by agent type (research agent, coding agent, customer support agent). Know which agents are expensive.
Multi-Agent Coordination
Track conversations between agents. See how supervisor agents delegate to worker agents. Understand multi-agent latency.
Granular Spend Tracking
Know exactly where every dollar goesâby user, tool, agent, or any custom dimension
Spend by User
Track costs per user_id. Find power users. Set per-user budgets and alerts.
Spend by Tool
See costs for Claude Code, browser automation, RAG retrieval, image generation. Optimize expensive tools.
Spend by Agent
Compare costs across agent types: customer support vs. code review vs. research. Know your unit economics.
Spend by Any Metadata
Group by customer_tier, feature_flag, environment, team, projectâanything you tag. Infinite flexibility.
Frequently Asked Questions
What is production-grade observability for AI agents?
Production-grade observability for AI agents means tracking multi-step workflows (planning, tool selection, execution, synthesis), debugging tool calls, measuring cost by agent/user/tool, monitoring RAG quality, and getting p50/p95/p99 latency for every component. Requesty shows you exactly where agents fail, where they spend money, and how to optimize them.
Can I track spend by individual users or tools like Claude Code?
Yes. Requesty lets you group costs by user_id, tool_name (Claude Code, browser, file system, API), agent_type, or any custom metadata you send. You can see exactly how much each user costs, which tools are expensive, and set per-user budgets with alerts.
How does Requesty help debug multi-step agent workflows?
Requesty traces every step of agent workflows: planning â tool selection â execution â synthesis. You see inputs/outputs for each step, latency breakdowns, failure points, and which tools were called. When an agent fails, you can replay the entire workflow and see exactly what went wrong.
How is Requesty different from application monitoring tools like Datadog or New Relic?
Traditional APM tools track infrastructure metrics. Requesty tracks AI-specific signals: token usage, cost per agent/tool, RAG retrieval quality, tool call success rates, multi-turn conversations, and agent-specific latency. We also provide automated evals (relevance, toxicity) and guardrails that APM tools don't have.
Does Requesty support OpenTelemetry?
Yes. Requesty exports traces in OpenTelemetry format and can ingest OTel traces from your existing instrumentation. This means you can use Requesty alongside your current observability stack.
What RAG metrics does Requesty track?
Requesty tracks recall@k (how many relevant docs were retrieved), context hit rate (how often retrieved context was used), citation coverage (% of response supported by sources), source diversity, and retrieval latency. These help you debug and optimize RAG pipelines.
Can I see which tools agents are using most?
Yes. Requesty tracks every tool invocation (Claude Code, browser, file system, API calls, RAG retrieval) with usage counts, success rates, average latency, and cost per tool. You can see which tools agents prefer and which are causing failures.
How do I track costs for multi-agent systems?
Tag each agent with agent_type metadata (supervisor, worker, researcher, coder). Requesty automatically groups costs by agent type and shows you inter-agent communication costs. You'll see which agents are expensive and how delegation affects total cost.
What about agent loop detection and infinite loops?
Requesty tracks agent step counts and loop patterns. Set alerts when an agent exceeds N steps or when costs spike unexpectedly. See visualization of agent loops to debug why agents get stuck.
Can I group metrics by user feedback?
Yes. Send thumbs up/down or custom satisfaction scores with your requests. Requesty will group latency, cost, and quality metrics by feedback score so you can see which responses users liked/disliked and why.
What alerts does Requesty support?
Proactive alerts via Slack, email, or PagerDuty when latency spikes, error rates increase, costs exceed budget, quality scores drop, or agents loop infinitely. Set thresholds per model, team, agent, or environment.
How do guardrails work in Requesty?
Guardrails run in real-time before requests reach your models. We detect and block: PII (SSN, credit cards, emails), prompt injection attempts, jailbreaks, toxicity, and off-topic prompts. You configure which rules apply per endpoint.