Agent Health Checks

Idea In 30 Seconds

Health checks for AI agents show whether the system is truly ready for traffic right now.

They help detect degradation before incident: slow tools, timeouts, queue issues, or LLM-layer failures.

Without health checks, teams often see only the failure itself, not the early signals before it.

Core Problem

A system can be formally "up" while already unstable in practice.

API responds, runs start, but some steps already degrade: timeout rate grows, success rate drops, queue time increases. Without health checks, this is usually visible only after cascading failures.

Next, we break down how to read these signals and find which component enters degradation first.

In production this often looks like:

• everything looks "working" externally, but p95 latency is already above SLO;
• one tool starts returning timeout more often, but the issue is still unclear;
• synthetic run starts failing before main traffic;
• team reacts only after partial outage.

That is why the health layer should be monitored separately, not only through general run metrics.

How It Works

Health checks are usually built on two levels:

• component checks (tool_available, llm_reachable, queue_ok, db_ok);
• end-to-end checks (synthetic_run_ok, critical_workflow_ok).

These signals answer "is the system healthy right now". Logs and tracing are needed to explain why a concrete check failed.

Up != healthy. A system can be reachable but already losing the ability to complete tasks.

Health checks are the earliest system signal. They appear before degradation becomes visible in latency or errors.

A drop in synthetic_run_success_rate usually precedes growth in timeout_rate and degradation in p95 latency.

Typical Production Health Metrics

health_score is usually computed on dashboard/health-service level as an aggregate of multiple checks, not as one "magic" metric.

To keep health metrics useful, they are usually segmented by release, region, workflow, and component type.

Important: do not add high-cardinality fields (run_id, request_id, user_id) to labels, or metric storage will overload quickly.

How To Read The Health Layer

What is checked -> what degrades -> which component breaks the workflow. These are three levels you should always read together.

Focus on time trends and release-to-release differences, not a one-off failure of one check.

Now look at signal combinations:

• health_check_pass_rate down + timeout_rate up -> system enters unstable mode;
• synthetic_run_success_rate down + degraded_component_count up -> issue already impacts critical workflow;
• queue_time_p95 up + run_count up -> capacity shortage, scaling needed;
• health_score down + error_rate up -> high incident risk in near term;
• tool_check_latency_p95 up + synthetic_run_success_rate down -> bottleneck in external tool layer.

When To Use

A full set of health checks is not always required.

For an early prototype, basic API availability check can be enough.

But detailed health checks become critical when:

• the system is already in production;
• there are stability SLO/SLA targets;
• the agent depends on multiple external tools or queues;
• early alerts are needed before mass failure starts.

Implementation Example

Below is a simplified Prometheus-style health-check loop. It shows baseline approach: component checks, synthetic run, and aggregated health score.

import time
from dataclasses import dataclass
from prometheus_client import Counter, Gauge, Histogram

# perf_counter() is used for precise monotonic latency measurements
HEALTH_CHECK_TOTAL = Counter(
    "agent_health_check_total",
    "Total health checks by check name and status",
    ["check", "status", "release"],
)

HEALTH_CHECK_LATENCY_MS = Histogram(
    "agent_health_check_latency_ms",
    "Health check latency in milliseconds",
    ["check", "release"],
    buckets=(10, 20, 50, 100, 250, 500, 1000, 2000),
)

SYNTHETIC_RUN_TOTAL = Counter(
    "agent_synthetic_run_total",
    "Total synthetic runs by status",
    ["status", "release"],
)

DEGRADED_COMPONENT_TOTAL = Counter(
    "agent_degraded_component_total",
    "Total degraded component detections",
    ["component", "reason", "release"],
)

HEALTH_SCORE = Gauge(
    "agent_health_score",
    "Aggregated health score in range 0..1",
    ["release"],
)


@dataclass
class CheckResult:
    ok: bool
    reason: str = "ok"


def run_health_checks(checks, run_synthetic, release="2026-03-22"):
    total = len(checks)
    failed = 0

    for check_name, check_fn in checks.items():
        started_at = time.perf_counter()
        status = "error"  # default for any failure path
        try:
            result = check_fn()  # -> CheckResult
            status = "ok" if result.ok else "fail"
            if not result.ok:
                failed += 1
                DEGRADED_COMPONENT_TOTAL.labels(
                    component=check_name,
                    reason=result.reason,
                    release=release,
                ).inc()
        except Exception as error:
            failed += 1
            DEGRADED_COMPONENT_TOTAL.labels(
                component=check_name,
                reason=type(error).__name__,
                release=release,
            ).inc()
        finally:
            HEALTH_CHECK_TOTAL.labels(check=check_name, status=status, release=release).inc()
            HEALTH_CHECK_LATENCY_MS.labels(check=check_name, release=release).observe(
                (time.perf_counter() - started_at) * 1000
            )

    try:
        synthetic_ok = run_synthetic()
        SYNTHETIC_RUN_TOTAL.labels(
            status="ok" if synthetic_ok else "fail",
            release=release,
        ).inc()
        if not synthetic_ok:
            failed += 1
    except Exception:
        SYNTHETIC_RUN_TOTAL.labels(status="error", release=release).inc()
        failed += 1

    # 1.0 = all checks passed, 0.0 = all failed
    # +1 for synthetic run
    denominator = total + 1
    health_score = max(0.0, 1.0 - (failed / max(1, denominator)))
    HEALTH_SCORE.labels(release=release).set(health_score)
    return health_score

This is how health metrics can look on a real dashboard:

Investigation

When a health alert fires:

1. find degraded check or workflow;
2. inspect problematic runs in tracing;
3. check timeout, stop_reason, and tool failures in logs;
4. find root cause (tool, LLM, queue, routing, release regression).

Common Mistakes

Even when health checks are added, they often fail to help because of common mistakes below.

Only ping/availability check exists

API availability does not guarantee agent workflow actually works. Without synthetic run, hidden degradation is easy to miss.

No checks for external dependencies

If tools, queues, or DB are not included in health checks, system can look "healthy" until first incident. In this case it is hard to quickly localize tool failure.

No breakdown by workflow and release

Without this, it is hard to understand which release or scenario degraded system state.

No alerts on health-score degradation

Without alerts, health checks become passive telemetry. This makes it easy to miss early multi-agent chaos signals.

High-cardinality labels

Adding run_id, request_id, or session_id to labels quickly overloads metric backend. Keep this data in logs and tracing.

Self-Check

Below is a short checklist of baseline health checks before release.

FAQ

Q: How are health checks different from regular metrics?
A: Metrics show trend, while health checks give a fast "ready or not right now" answer for concrete scenarios.

Q: What is the minimum health-check set to start with?
A: Start with llm_reachable, tool_available, queue_ok, and one synthetic run for critical workflow.

Q: Why is synthetic run so important?
A: It validates full execution path, not a single component. This best reflects real production state.

Q: If health score drops but API still responds, is it already incident?
A: It is early degradation. Investigation should start immediately before issue becomes mass failure.

Related Pages

Next on this topic:

• Observability for AI Agents — base model of tracing, logs, and metrics.
• Agent Metrics — general production system signals.
• Alerting In AI Agents — how to build early notifications.
• Agent Tracing — how to localize the step that breaks health check.
• Agent Latency Monitoring — how to detect speed degradation before incident.