Modern systems are powered by agents and flows—background workers, orchestration pipelines, AI agents, cron jobs, and event-driven microservices that quietly keep the business running. When these behind-the-scenes components misbehave, customer-facing systems degrade, costs spike, and incidents multiply.
This guide shows you how to monitor agents and flows end-to-end using Grafana and Sentry, with OpenTelemetry as the connective tissue. You’ll learn what to instrument, which dashboards matter, how to reduce alert noise, and how to move from detection to automated recovery.
If you’re new to either platform, this practical Sentry 101 guide and this walkthrough on Grafana + BigQuery for unified technical dashboards are excellent companions to this article.
What Do We Mean by “Agents” and “Flows”?
- Agents: Long-running or on-demand workers that pull from queues, call APIs, process data, or act autonomously (including AI agents). Examples: background job processors, vector indexers, web crawlers, LLM tool-using agents, IoT data workers.
- Flows: Multi-step executions that orchestrate tasks. Examples: Airflow DAGs, Temporal workflows, streaming pipelines, ETL/ELT jobs, RAG pipelines, or incident-response playbooks.
Why they’re tricky to observe:
- Failures often hide in non-user paths.
- Success can mask degraded performance (e.g., retries, timeouts, back-pressure).
- Cost and reliability are tightly coupled to throughput and concurrency.
- You must correlate logs, metrics, and traces across many moving parts.
Why Grafana + Sentry Is a Powerful Combination
- Sentry specializes in application-level visibility:
- Error tracking with rich context and grouping
- Performance tracing and release health
- Ownership, issue workflow, and developer-friendly root-cause analysis
- Grafana excels at system-wide observability:
- Unified dashboards across time-series metrics, logs, traces, and business KPIs
- Alerting, SLOs, and correlation at scale
- Works with Prometheus, Loki, Tempo, BigQuery, Snowflake, and many more
Together, they cover developer-centric debugging (Sentry) and operations-centric monitoring and analytics (Grafana), giving you a shared truth across teams.
A Reference Architecture That Just Works
Use OpenTelemetry (OTel) to standardize instrumentation and context propagation:
- Traces:
- OTel SDKs in agents and orchestrators
- Export to Sentry for APM and to a tracing backend (Grafana Tempo/Jaeger) for deep correlation
- Metrics:
- Prometheus exporters (system + custom business metrics)
- Scrape and store in Prometheus/Mimir/BigQuery (Grafana visualizes all)
- Logs:
- Structured JSON logs with trace_id and span_id
- Ship to Loki or your central log platform
- Correlation:
- Ensure trace context flows across HTTP/gRPC/queue boundaries
- Use exemplars to link metrics panels to traces
Result: Developers fix code faster with Sentry; operators see the big picture in Grafana; everyone clicks from a red metric to the exact problematic trace and log lines.
Step-by-Step Implementation
1) Instrument your agents
- Add OTel SDKs to workers and orchestrators.
- Set service.name, deployment.environment, and version to match release health in Sentry.
- Use semantic conventions (e.g., messaging, db, http) for consistent spans.
2) Send traces where they’re most useful
- Export to Sentry for error/performance triage and release correlation.
- Mirror to your tracing backend (Tempo/Jaeger) for Grafana-native trace exploration.
3) Emit the right metrics
- System: CPU, memory, file descriptors, thread pools.
- Work: throughput, queue depth, consumer lag, retry count, DLQ size, success ratio.
- Timing: queue wait time, step-level durations, external API latency.
- Cost: job cost estimates, token usage (for LLM agents), egress quotas.
4) Structure your logs
- Log in JSON and include trace_id and span_id.
- Add labels for flow_id, step_name, tenant, and region to filter quickly.
- Sanitize PII at source (hash or redact).
5) Build dashboards that answer questions
- Start with golden signals (errors, latency, saturation, traffic).
- Layer in business KPIs for context (e.g., processed documents, invoices matched, tasks completed).
6) Alert on symptoms, not noise
- Use SLOs with multi-window, multi-burn-rate alerts.
- Deduplicate alerts, escalate by severity, and link to runbooks.
7) Close the loop with automation
- Trigger remediation flows when SLOs breach.
- For example, re-route traffic, scale workers, or run a rollback workflow.
- Explore how to build self-healing systems with Sentry and Temporal.
The Metrics, Traces, and Logs That Matter
Focus on these core signals for agents and flows:
- Throughput and backlog
- Jobs per minute, tasks queued, consumer lag
- Reliability and quality
- Success/failure rate, DLQ volume, retry depth, error types
- Latency at each step
- Queue wait, execution time, external API round-trips, database time
- Resource saturation
- CPU/memory, open connections, threads, file handles
- Cost and efficiency
- Cost per job, compute per task, LLM token usage, cache hit ratio
- Dependency health
- Upstream/downstream error rates, timeouts, rate limits
- Trace-level insights
- Longest spans, N+1 patterns, “hot” steps, retries inside spans
- Logs for context
- Structured event breadcrumbs, input/output sizes, sanitized parameters
Dashboards That Actually Drive Action
Create targeted Grafana dashboards (with drilldowns):
- Flow Overview
- Success rate, total runs, median and p95 latency
- Backlog/lag trend, throughput trend
- Error rate breakdown by step_name and exception type
- Agent Health
- CPU/memory, goroutines/threads, restarts, queue consumer lag
- Saturation signals (e.g., thread pool queue length)
- Reliability & SLOs
- Availability SLOs per flow and environment
- Burn rates (30m, 6h, 24h) and error budget remaining
- Cost & Performance
- Cost per job/tenant, tokens per request, cache hits
- Optimization candidates (expensive spans, frequent retries)
- Dependencies & External APIs
- Per-endpoint latency, 4xx/5xx, timeouts, rate-limit events
- Retries and backoff counts
Add exemplars so anyone can jump from a panel to the exact trace in Sentry or Tempo.
Alerting Without 3 a.m. Pager Fatigue
- SLO-based alerts first, symptom alerts second, component alerts last.
- Multi-burn-rate strategy:
- High burn = page immediately (acute incidents)
- Low burn = ticket + observe (slow burns)
- Deduplicate and route by ownership (service, flow, or team).
- Include runbook links and auto-create issues in your tracker from Sentry.
- Periodically prune and tune alerts; sunset those with no recent value.
Correlation That Saves Hours
- Add trace_id and span_id to logs and metrics labels.
- Use consistent naming for service.name, flow_id, and step_name across systems.
- Use context propagation (W3C Trace Context) across HTTP, gRPC, message queues.
- From Grafana:
- Click exemplars to open traces
- Pivot from traces to Sentry issues
- Review release health and regression trends
Security, Privacy, and Governance
- Redact secrets and PII at source (beforeSend in Sentry; OTel attribute processors).
- Role-based access with least privilege for dashboards and error payloads.
- Data retention tuned to compliance and cost.
- Clear ownership: who fixes what, in which environment, under which SLO.
Common Pitfalls (and How to Avoid Them)
- Uncorrelated signals: No trace_id in logs/metrics. Fix with OTel propagators.
- Alert floods: Too many “component”-level alerts. Move to SLOs and symptoms.
- Cardinality explosions: Unbounded labels (e.g., request_id). Whitelist labels.
- Shallow instrumentation: Only system metrics. Add business and step-level spans.
- No runbooks: Alerts without action steps. Attach tested, up-to-date runbooks.
A 30/60/90-Day Roadmap
- First 30 days
- Instrument OTel in two critical flows and one agent.
- Send traces to Sentry, metrics to Prometheus (or BigQuery), logs to Loki.
- Build Flow Overview and Agent Health dashboards in Grafana.
- Create two SLOs and set initial alerts.
- Next 30 days
- Roll out context propagation across services and queues.
- Add dependency panels and cost metrics.
- Introduce deduplicated, multi-burn-rate alerting.
- Start release health and regression tracking in Sentry.
- Next 30 days
- Expand to remaining flows; standardize labels and conventions.
- Add automated remediation for your top incident class.
- Review incidents and tune thresholds quarterly.
- Document ownership, runbooks, and on-call rotations.
What’s New and Important for 2026
- AI agent observability becomes first-class:
- Token usage, tool-call success, retrieval quality, and cost per outcome
- eBPF-based telemetry reduces manual instrumentation overhead
- OTel logs mature further, improving three-signal correlation
- SLOs shift from uptime to outcome (business KPIs tied to reliability)
- Automated remediation (via orchestration engines) moves from “nice-to-have” to standard
Further Reading
- Get the basics right with a developer-friendly Sentry 101 guide.
- Build dashboards that drive action with Grafana + BigQuery for unified technical observability.
- Go beyond detection with automated recovery using Sentry and Temporal for self-healing systems.
FAQ: Monitoring Agents and Flows with Grafana and Sentry
1) How do I decide what to measure first?
- Start with golden signals: latency, error rate, traffic, and saturation.
- Then add business metrics per flow (success rate, backlog, retries, DLQ size).
- Finally include dependencies (external APIs), cost (tokens, compute), and step-level timings.
2) Do I need both Sentry and Grafana?
- They complement each other. Sentry shines at app-level debugging, issue grouping, and release health for developers. Grafana excels at cross-system dashboards, SLOs, and alerts for operations. Together, you get deep and wide visibility.
3) What’s the simplest way to correlate logs, metrics, and traces?
- Use OpenTelemetry to propagate trace context. Add trace_id/span_id to logs. Use exemplars in metrics panels. Ensure consistent service.name and labels (flow_id, step_name) across all telemetry.
4) We use Airflow/Temporal. How should we instrument flows?
- Instrument the orchestrator and each task/step with OTel spans (include flow_id, step_name, tenant). Emit task durations, retries, and outcome metrics. Correlate with logs via trace_id. Consider automated remediation playbooks as part of incident response.
5) How do I avoid alert fatigue?
- Alert on SLO burn rates (both fast and slow). Deduplicate by service/flow. Route alerts to the owners listed in dashboards. Include runbooks. Review and prune regularly.
6) How can I monitor AI agents specifically?
- Add metrics for token usage, tool-call success rate, retrieval latency/quality (for RAG), cost per solved task, and refusal/hallucination proxies. Trace tool calls and external dependencies. Use Sentry for failure grouping and regression detection across releases.
7) What are best practices for PII and secret handling?
- Redact at source (Sentry beforeSend, OTel attribute processors). Only log hashes or token counts. Apply role-based access control to dashboards and payloads. Set retention limits per compliance needs.
8) Which dashboards should I build first?
- Flow Overview (success, latency, backlog), Agent Health (resources, restarts, lag), Reliability & SLOs (burn rate, error budget), and Dependencies & External APIs (latency and error breakdown). Add Cost & Efficiency later.
9) How do I estimate the cost of my flows?
- Track cost per job: tokens, compute seconds, storage reads/writes, egress. Attribute costs to tenants or products. Visualize as cost per successful outcome to guide optimization.
10) What’s a practical way to get value in 2 weeks?
- Instrument one critical flow with OTel; send traces to Sentry, metrics to Prometheus, logs to Loki. Build a Grafana Flow Overview dashboard with exemplars. Add one SLO alert. You’ll immediately reduce mean time to detect (MTTD) and mean time to resolve (MTTR).
By pairing Sentry’s developer-first visibility with Grafana’s cross-system observability—and standardizing on OpenTelemetry—you can make your agents and flows transparent, reliable, and cost-efficient. In 2026, that combination is no longer optional; it’s how modern teams keep complex systems calm, fast, and under control.
