53 — Health Check Endpoints for Agents

Concept overview

An agent health endpoint exposes a deliberately narrow claim about its ability to operate. Liveness asks whether the process can make local progress; readiness asks whether it should receive new work; startup asks whether initialization has completed. End-to-end synthetic checks ask a different question: can a representative task succeed across its dependencies?

Combining these signals into one deep probe creates dangerous feedback. A provider outage can make every agent fail liveness, causing restarts that destroy warm state without repairing the provider. Health handlers should be cheap, bounded, and independent of the same saturated execution pool they are diagnosing.

Why it matters in production

Proof of concept

A prototype can expose a process heartbeat and a manual sample-task check to reveal the distinction between reachability and usefulness.

Production

Production defines separate endpoints, startup allowances, readiness thresholds, and graceful draining for active tasks.

Enterprise scale

At fleet scale, probe traffic itself needs budgeting, synthetic tests need safe test tenants, and alerting must distinguish local failure from shared dependency incidents.

Architecture components

Liveness handler

Checks local event-loop or worker progress without calling remote model providers.

Readiness evaluator

Reports configuration validity, required local resources, and admission capacity.

Startup gate

Prevents normal probes from interrupting legitimate initialization or model loading.

Synthetic task runner

Exercises an approved read-only workflow with known expected outcomes.

Traffic controller

Removes unready instances and respects draining before termination.

The architecture, mapped

flowchart TD
 K["Container supervisor"] --> S["Startup complete"]
 S --> L["Local progress probe"]
 L --> R["Restart stalled process"]
 T["Traffic controller"] --> Q["Ready to admit work"]
 Q --> D["Drain unready instance"]
 M["Synthetic task monitor"] --> A["Agent plus dependencies"]
 A --> I["Capability incident"]

stateDiagram-v2
 [*] --> Starting
 Starting --> Ready: Initialization and warmup pass
 Ready --> Draining: Shutdown or capacity withdrawal
 Draining --> Stopped: Active work finishes
 Ready --> Unready: Cannot admit new work
 Unready --> Ready: Admission recovers
 Ready --> Failed: Local progress stalls
 Failed --> Starting: Supervisor replaces instance
 Stopped --> [*]

Runtime flow

1. 1

   Initialize under startup gate

   Load configuration, connect durable state, and complete any required model warmup.

2. 2

   Publish readiness

   Allow traffic only when the instance can admit work under its documented dependency policy.

3. 3

   Probe local progress

   Respond to liveness using a cheap signal that detects an unrecoverable local stall.

4. 4

   Run synthetic capability checks

   Periodically execute a separate safe task and classify dependency and semantic failures.

5. 5

   Drain before replacement

   Stop accepting new tasks, checkpoint active work, and honor a bounded shutdown grace period.

6. 6

   Correlate fleet signals

   Compare per-instance failures with regional dependency status before triggering broad remediation.

Customer support agent health during a billing outage

Telecom · Illustrative enterprise scenario, not a reported customer outcome.

Business situation

A support platform runs many agent replicas that answer product questions and retrieve customer bills.

The problem

A billing outage affects one capability while product information remains available.

Agent requirement

Continue safe product assistance and clearly defer billing work without restarting every replica.

Architecture

Local probes feed the container platform; a capability monitor checks a synthetic billing account and publishes degraded billing status.

Runtime

• A billing synthetic fails while local liveness remains healthy.
• The router disables billing-dependent tasks and leaves product-question capacity available.

Failure scenario

If the event loop actually stalls, the liveness controller replaces only that instance after its failure threshold.

Operational impact

Track capability availability, false restart rate, and task loss during drain; process uptime alone cannot represent user-visible availability.

Architecture tradeoffs

Failure scenarios & recovery

Common architecture mistakes

Calling the model on every liveness probe

Use local progress for liveness and a scheduled synthetic for model capability.

Returning detailed secrets in health output

Expose minimal public status and protect diagnostic details.

Killing workers before checkpointing

Make readiness fail first, then drain active jobs before termination.

When to choose this pattern

Choose it when

• Use for agents managed by containers or process supervisors.
• Use when one agent exposes several independently degradable capabilities.
• Use before enabling automatic replacement or autoscaling.

Avoid it when

• Do not use liveness as a business-quality score.
• Avoid expensive inference on high-frequency probes.
• Avoid declaring unready for optional dependencies with valid fallback behavior.

Architecture checklist

• Document each endpoint claim.
• Keep liveness free of remote calls.
• Set startup budgets from measurements.
• Bound probe execution time.
• Test graceful drain with active tool calls.
• Monitor synthetic tasks separately from container status.

Related concepts

• 17 — Agent Lifespan Management
• 49 — Timeout Policies
• 52 — Model Redundancy / Multi-Provider Failover
• 59 — Cascade Failure Prevention
• 60 — Chaos Engineering for AI
• 91 — GPU Cluster Orchestration (Kubernetes/KServe)

Primary references

• Kubernetes: Liveness, readiness and startup probes