Health Checks

Health checks are categorized by their purpose. Liveness probes determine if an agent process is running (e.g., the container is up). Readiness probes verify if an agent is fully initialized and capable of handling work (e.g., its model is loaded, dependencies are connected). A live agent may not be ready, but a ready agent must be live. This distinction is critical for graceful startup, shutdown, and load balancing in orchestrated systems.

Effective health checks operate at multiple levels of the stack:

• Infrastructure Layer: CPU, memory, and network connectivity of the host or container.
• Agent Process Layer: Is the agent executable running and responsive to a simple ping?
• Functional Capability Layer: Can the agent perform its core task? For an LLM-based agent, this might involve a simple inference test; for a tool-calling agent, it could be a mock API call.
• Dependency Health Layer: Are downstream services (databases, vector stores, APIs) that the agent relies on accessible and performing within expected latency bounds?

Health checks are not binary pass/fail signals but are governed by configurable policies to prevent flapping and false positives.

• Failure Threshold: The number of consecutive failed checks required before an agent is declared unhealthy (e.g., 3 failures).
• Success Threshold: The number of consecutive successful checks required to transition from unhealthy to healthy.
• Initial Delay/Grace Period: A wait time after agent startup before probes begin, allowing for initialization.
• Timeout & Period: The time to wait for a probe response (timeout) and the frequency of execution (period).

The orchestrator uses health check results to automate agent lifecycle management. Upon a failure:

1. The agent is marked unhealthy and removed from the load balancer pool.
2. The orchestrator may attempt a restart of the agent instance (according to a restart policy).
3. If restarts fail repeatedly, the agent may be rescheduled onto a different node (if in a clustered environment).
4. Alerts are triggered for operator intervention if automated recovery fails. This creates a self-healing loop, a core tenet of resilient multi-agent systems.

The most advanced health checks simulate real user transactions or workflows. Instead of checking if an agent can work, they verify it does work correctly. For a multi-agent workflow, this involves:

• Injecting a synthetic, idempotent task into the orchestration queue.
• Tracing its execution path through the relevant agent call graph.
• Validating the final output against an expected result.
• Measuring end-to-end latency. This provides the highest-fidelity signal of system health but is more complex to implement and maintain.

Health status is not an isolated metric. It is enriched and validated by correlating with the Golden Signals of observability:

• Latency: Are health check response times degrading?
• Traffic: Is the agent receiving its expected share of work?
• Errors: Are errors in the agent's logs correlated with health check failures?
• Saturation: Is the agent's resource utilization (CPU, memory, I/O) at a level that impacts health? This correlation turns a simple 'up/down' status into a diagnostic tool for root cause analysis.

A method for profiling requests as they propagate through a distributed system. In a multi-agent network, a trace captures the end-to-end journey of a user request or agent-initiated task.

• Composed of spans, which represent individual units of work (e.g., a single agent's reasoning cycle or tool call).
• Essential for diagnosing latency bottlenecks and understanding complex interaction dependencies between agents.
• Frameworks like OpenTelemetry (OTel) provide standardized instrumentation for generating traces.

A target level of reliability or performance for a service, defined as a percentage over a time period. For agent orchestration, SLOs translate business requirements into measurable technical outcomes.

• Examples: "Agent workflow completion success rate > 99.5% over 30 days" or "P95 end-to-end task latency < 2 seconds."
• Health checks are the primary mechanism for measuring SLO compliance by probing for successful outcomes.
• The Error Budget (1 - SLO) quantifies the allowable unreliability, guiding the pace of deployments and changes.

Four high-level metrics for monitoring any distributed service: Latency, Traffic, Errors, and Saturation. They provide a holistic, first-pass health assessment.

• Latency: Time to complete agent tasks or respond to probes.
• Traffic: Rate of requests or messages flowing through the agent network.
• Errors: Rate of failed health checks, agent crashes, or unsuccessful tool executions.
• Saturation: How "full" a resource is (e.g., agent queue depth, CPU/memory usage of the orchestration layer). Health checks directly feed into the Errors signal and inform Latency.

A fault-tolerance design pattern that prevents cascading failures. It wraps calls to a potentially failing component (like an agent or external API).

• Operates in three states: Closed (normal operation), Open (failing fast, no calls made), and Half-Open (testing for recovery).
• Health check failures can trip the circuit breaker to Open, stopping traffic to a malfunctioning agent.
• After a timeout, it moves to Half-Open, allowing a probe (a health check) to test if the service has recovered before closing the circuit again.

A holding queue for messages that cannot be delivered or processed after repeated failures. In agent systems, DLQs handle undeliverable inter-agent messages or tasks that cause persistent errors.

• Acts as a safety net, preventing poison pills from blocking entire workflows.
• The presence of items in a DLQ is a critical health signal. Automated alerts should trigger for manual inspection and recovery.
• Differentiated from a retry queue by its finality; items are not automatically re-processed.

A visual or data representation mapping the sequence of interactions and message flows between agents during a specific task execution. It is the topological output of distributed tracing for a multi-agent system.

• Reveals the orchestration workflow and dependencies, showing which agents communicated and in what order.
• Critical for debugging: a failing health check on one agent can be contextualized by seeing its upstream dependencies and downstream impacts.
• Enables performance analysis by identifying critical paths and bottlenecks in collaborative tasks.