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Observability in Cascade provides structured visibility into AI agent execution. It captures agent behavior, LLM interactions, tool invocations, and reasoning steps automatically without requiring manual instrumentation. Traces are captured at runtime and stored for analysis, enabling debugging, performance optimization, and behavioral understanding across agent workflows.
Cascade log interface showing detailed trace execution

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Why Observability for Agents

AI agents differ from traditional applications in execution characteristics:

Risk in reasoning chains

Unsafe behavior emerges in multi-step interactions and context propagation

Dynamic tool invocation

Agents make runtime decisions about which tools to use and when

Complex execution flows

Nested operations, parallel tasks, and async execution create intricate traces

Behavioral drift

Changes in reasoning patterns or model behavior go undetected
Observability in Cascade captures the execution graph, reasoning steps, and tool interactions needed to understand agent behavior, identify failures, evaluate safety policies, and detect security threats at runtime.

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What Cascade Tracks

Cascade automatically captures telemetry across agent execution.

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Traces

Hierarchical representation of agent execution flow.
  • Parent-child relationships between operations
  • Complete execution timeline from start to finish
  • Visual trace trees for understanding flow
  • Nested operation context to see how components interact

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LLM Calls

Every interaction with language models is captured in full detail.
  • Complete prompts with all variables resolved
  • System messages and instructions
  • User messages and conversation history
  • Full completion text
  • Token usage (input, output, total)
  • Estimated costs per model
  • Response latency in milliseconds
  • Temperature, top_p, and other sampling parameters
  • Model name and version
  • Provider information

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Tool Executions

Function and tool invocations with complete execution details.
MetricDescription
Input parametersAll arguments passed to the tool
Output resultsReturn values or data produced
Execution durationTime taken to complete
Success/failure statusWhether the tool executed successfully

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Use Cases

Observability data supports several workflows: Debugging - Replay exact execution flows to understand failures and unexpected behavior. Trace context shows the full sequence of operations leading to an error. Security threat detection - Identify adversarial manipulation including prompt injection, data exfiltration, and context poisoning. Full prompt and completion traces reveal attacks that bypass traditional security controls. Behavioral validation - Detect drift in agent reasoning, tool selection, and output patterns. Statistical baselining and semantic analysis surface changes that indicate misalignment or degradation. Incident investigation - Replay exact execution flows to understand safety violations, security incidents, and unexpected behavior. Trace context shows the complete sequence of operations, reasoning steps, and tool invocations that led to an event.

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Next Steps

To get started with observability in Cascade:
  1. Instrument your agent with the Cascade SDK
  2. Wrap your LLM client with wrap_llm_client()
  3. Use trace_run() to group operations into logical workflows
  4. View traces in the Cascade Dashboard
Continue to Traces to learn about trace structure and hierarchies, or jump to Data Flow to understand how telemetry is captured and transmitted.