0001. Observability-First Architecture

Status: Proposed
Date:
Categories:
Architecture

0001. Observability-First Architecture

Date: 2025-01-31

Status

Proposed

Context

Multi-agent systems are inherently complex, distributed, and non-deterministic. When agents communicate asynchronously, debugging becomes extremely challenging:

  • Traditional logging is insufficient - logs from different agents are interleaved and lack correlation
  • Distributed tracing helps but needs to be built in from the start
  • Reproducing issues is nearly impossible without understanding the exact sequence of interactions
  • Performance bottlenecks are hard to identify without proper metrics

We need a server approach that makes agent systems observable and debuggable by design, while remaining agnostic to how users choose to persist or analyze their data.

Decision

We will build observability into Caxton from the ground up using OpenTelemetry standards, structured logging, and correlation tracking, while remaining storage-agnostic.

Key aspects:

  • Every message includes trace and span IDs for distributed tracing
  • All log entries use structured format with consistent fields
  • Correlation IDs link related messages across agents
  • Metrics track performance at multiple levels (agent, message type, tool usage)
  • The framework emits telemetry but doesn’t mandate storage solutions
  • Users can export to any OpenTelemetry-compatible backend

Consequences

Positive

  • Complete observability: Every interaction can be traced and analyzed
  • Storage flexibility: Users choose their own backends (Jaeger, Datadog, Honeycomb, etc.)
  • Industry standards: OpenTelemetry is widely supported and understood
  • Performance insights: Metrics help identify bottlenecks before they become problems
  • Debugging efficiency: Correlation IDs make it easy to follow request flows
  • Production readiness: Same observability in dev and production

Negative

  • Initial complexity: Developers must understand distributed tracing concepts
  • Performance overhead: Telemetry collection adds some latency (typically < 1%)
  • Configuration burden: Users must set up their own telemetry backends
  • Data volume: High-cardinality traces can be expensive to store
  • Learning curve: Teams need to learn effective debugging with traces

Neutral

  • No built-in storage: Framework doesn’t include a database, which is both flexible and requires setup
  • Sampling decisions: Users must configure sampling rates for cost/visibility trade-offs
  • Tool diversity: Many backend options available, but no single “best” choice

Implementation Notes

// Every message automatically includes tracing context
pub struct FipaMessage {
    pub id: MessageId,
    pub trace_id: TraceId,
    pub span_id: SpanId,
    pub correlation_id: CorrelationId,
    // ... other fields
}

// Structured logging with consistent fields
info!(
    agent_id = %agent.id,
    message_type = msg.performative(),
    correlation_id = %msg.correlation_id,
    duration_ms = elapsed.as_millis(),
    "Message processed successfully"
);

// Automatic span creation for operations
#[instrument(skip(self, msg))]
pub async fn handle_message(&self, msg: FipaMessage) -> Result<(), Error> {
    // Implementation automatically traced
}

References