Short answer. An AI agent is a chain of dependencies: LLM API документацию, external tools (search, APIs), stores and background workers. Any link failing breaks the agent. Monitoring an agent comes down to three jobs: check each dependency's availability over HTTP, cover background agents with heartbeat, and watch call cost and latency. enterno.io provides the external availability layer from RU, EU and US.
What the agent's risk surface is made of
Plenty can fail in a typical agent:
- The LLM API — 429, 5xx, rising latency;
- external tools — a search or domain API the agent calls;
- stores — vector DB, cache, queue;
- the worker itself — hung, crashed, not restarted.
Three layers of agent monitoring
- Dependency availability — HTTP checks of health endpoints.
- Agent liveness — heartbeat of background processes.
- Economics — tokens, cost and call latency.
An agent can be "running" as a process yet silently degrade if one tool responds slowly or with an error. External checks catch this before the user does.
| Dependency | Typical failure | How to monitor |
|---|---|---|
| LLM API | 429, 5xx, rising latency | HTTP monitor of the health endpoint |
| External tool | Errors or empty response | HTTP monitor |
| Vector DB / cache | Down, slow response | HTTP monitor + latency |
| Background worker | Hung, not restarted | Heartbeat |
Health-checking dependencies
Set up a simple check of each critical dependency and put it under monitoring:
# LLM API
curl -o /dev/null -s -w "llm %{http_code} %{time_total}s\n" \
https://api.example-llm.com/v1/health
# An agent tool (e.g. a search API)
curl -o /dev/null -s -w "tool %{http_code} %{time_total}s\n" \
https://api.search-tool.com/healthAdd each such check to enterno.io as an HTTP monitor at a 1-minute interval with alerts when the code is ≠ 200.
Heartbeat for a background agent
If the agent runs in the background (on a schedule or as a queue worker), have it "Ping" a heartbeat endpoint at the end of each cycle:
# At the end of a successful agent cycle
curl -fsS https://enterno.io/api/heartbeat/YOUR_TOKEN \
-o /dev/null && echo "heartbeat sent"If the ping doesn't arrive within the expected window, enterno.io raises an incident — the classic dead man's switch.
Cost control
- Log tokens and cost at every agent step.
- Set budget thresholds and alerts on abnormal growth.
- Remember: a dependency being down often triggers retries — that's hidden cost growth.
The line: where enterno.io fits, where it doesn't
enterno.io is the external availability and heartbeat layer. It doesn't inspect the reasoning chain or score answer quality — that needs tracing and eval (Langfuse and similar). But it's availability that most often takes an agent down in production, and enterno.io covers that layer fully.
FAQ
How is agent monitoring different from site monitoring?
An agent has more external dependencies and background processes — so you add heartbeat and checks of several endpoints.
How do I catch "silent" degradation?
Watch the latency of health endpoints: rising response time is an early signal before a full failure.
What if the agent runs on cron?
A perfect heartbeat case: ping at the end of the cycle, alert on a miss.
Can I check from Russia?
Yes, checks run from ru-msk, with EU and US on paid tiers.
Cover your agent: create HTTP checks on the monitors page and connect heartbeat for background processes.
Related: monitoring AI/LLM APIs, best API monitoring tools, monitoring guide.