Unified Logging for AI Workflows

AI workflows are inherently multi-step, multi-tool, and often multi-agent. A single user request might trigger a planning phase, three tool invocations, a reflection step, and a final synthesis. Each step produces logs. When things go wrong, diagnosing the failure means tracing through logs from multiple components, correlating timestamps, and reconstructing the execution sequence.

Without structured logging, this diagnosis is painful. Unstructured log lines like "Processing request..." and "Done" tell you nothing about what happened between them. With structured logging, every log entry carries context: which agent, which step, which tool, what input, what output, how long it took, and whether it succeeded.

The difference between debuggable and undebugable AI workflows is the logging architecture. Here's how to build one that works.

Key Takeaways

• Structured JSON logs enable machine parsing, which lets AI itself help debug AI workflows
• Correlation IDs link every log entry in a multi-step workflow to a single request trace
• Log levels must be meaningful: DEBUG for tool I/O, INFO for step transitions, WARN for retries, ERROR for failures
• Separate data plane from control plane logs to manage volume without losing signal
• Log aggregation with search and filtering is non-negotiable for multi-agent systems

Why AI Workflows Need Different Logging

Traditional application logging assumes a relatively linear execution path: request arrives, business logic runs, response returns. The log entries follow this path sequentially.

AI workflows are different in several ways:

Non-deterministic execution. The same input can produce different tool call sequences depending on the model's reasoning. Logs must capture the actual path, not just the intended path.

High volume per request. A single user interaction might generate hundreds of log entries across planning, tool use, and synthesis. Without filtering, the signal drowns in noise.

Multi-component execution. An agent orchestrator, multiple tools, external APIs, and possibly sub-agents all produce logs. These logs need correlation to reconstruct the full picture.

Retry and fallback. AI workflows frequently retry failed tool calls or fall back to alternative approaches. Logs must distinguish between the initial attempt and retries, and between the primary approach and fallbacks.

Designing the Log Schema

A well-designed log schema for AI workflows includes these fields:

{
  "timestamp": "2026-07-22T14:30:45.123Z",
  "level": "INFO",
  "correlation_id": "req-abc123",
  "agent_id": "code-reviewer",
  "step": "tool_invocation",
  "step_index": 3,
  "tool": "Read",
  "input_summary": "Reading src/auth/handler.ts",
  "output_summary": "247 lines, TypeScript",
  "duration_ms": 45,
  "success": true,
  "metadata": {
    "model": "claude-opus-4-6",
    "tokens_in": 1200,
    "tokens_out": 0,
    "cost_usd": 0.018
  }
}

Key design decisions:

Correlation ID ties every log entry from a single workflow execution together. When debugging, filter by correlation ID to see the complete trace.

Agent ID identifies which agent produced the log. In multi-agent systems, this is essential for understanding which component is responsible for a failure.

Step and step_index provide sequential ordering within the workflow. Step names describe what's happening ("tool_invocation", "planning", "synthesis"). Step index provides ordering when timestamps alone are insufficient (concurrent steps have the same timestamp).

Input and output summaries capture enough context to understand what happened without logging the full input and output, which can be enormous. The summary should answer "what was the tool asked to do?" and "what did it return?" without including the entire file contents or API response.

Duration enables performance analysis. A tool invocation that usually takes 50ms but took 5000ms indicates a problem worth investigating.

Metadata captures model-specific information (which model, how many tokens, what cost) that's useful for cost analysis and debugging model-specific behaviors.

Log Level Strategy

Log levels in AI workflows need more precision than the standard DEBUG/INFO/WARN/ERROR because the volume at each level is much higher than in traditional applications.

TRACE (use sparingly): Full tool inputs and outputs. Enable only when debugging specific issues. A single workflow can produce megabytes of TRACE logs.

DEBUG: Tool invocation summaries, model prompt fragments, intermediate results. Useful for understanding what the AI did and why.

INFO: Step transitions, workflow start/end, significant decisions. The level for production monitoring. A normal workflow produces 10-20 INFO entries.

WARN: Retries, fallbacks, degraded performance, approaching rate limits. These indicate potential problems that haven't yet caused failures.

ERROR: Failed tool invocations, model errors, unrecoverable failures. These require investigation.

FATAL: System-level failures that prevent any workflow from executing. Infrastructure down, credentials expired, disk full.

The key discipline is consistency. If tool invocations are logged at DEBUG, all tool invocations are logged at DEBUG. If retries are logged at WARN, all retries are logged at WARN. Inconsistent level usage makes filtering unreliable.

Correlation Across Components

A multi-agent workflow might span:

1. An orchestrator that plans the workflow
2. Multiple tool executors that perform actions
3. External APIs that provide data
4. Sub-agents that handle delegated tasks

Each component has its own logging. Correlation IDs connect them.

The orchestrator generates a correlation ID when a workflow begins and passes it to every component. Each component includes the correlation ID in every log entry. When debugging, filtering by correlation ID shows the complete workflow trace across all components.

For sub-agent delegation, use hierarchical correlation IDs: req-abc123/sub-1, req-abc123/sub-2. This preserves the relationship between parent and child workflows while allowing independent filtering of each sub-workflow.

Logging for AI Self-Debugging

A powerful use of structured logs is feeding them back to AI for debugging. When a workflow fails, the structured log can be formatted as context for Claude Code to analyze:

"Here's the structured log of a failed workflow. Correlation ID req-abc123. The workflow was supposed to review a PR and produce a summary. It failed at step 5. Diagnose the failure."

AI analyzes the log systematically: what inputs did each step receive, what outputs did each step produce, where did the actual execution deviate from the expected execution, and what could cause that deviation.

This self-debugging capability only works with structured logs. Unstructured text logs require the AI to parse free-form text, guess at field boundaries, and infer structure. Structured JSON logs are directly machine-readable.

For more on how AI handles complex debugging tasks, see The Great Crash Hunt: AI Detective and Core Dump Analysis Using AI.

Log Aggregation Architecture

For production AI workflows, logs need aggregation into a searchable system. The architecture:

Log emission. Each component writes structured JSON logs to stdout. Container orchestrators (Docker, Kubernetes) capture stdout automatically.

Collection. A log collector (Fluentd, Vector, Logstash) reads container logs, parses the JSON, and forwards to storage.

Storage. A log storage system (Elasticsearch, Loki, ClickHouse) indexes logs for fast search. Retention policies manage storage costs.

Querying. A query interface (Kibana, Grafana, custom) enables searching by correlation ID, agent ID, log level, time range, and free text.

Alerting. Rules trigger notifications when error rates exceed thresholds, when specific error patterns appear, or when workflow durations exceed SLAs.

The most important capability is correlation ID search. Given a failed workflow, the operator enters the correlation ID and sees every log entry from every component in chronological order. This single capability makes most debugging straightforward.

Performance Logging

Beyond diagnostic logging, performance logging enables AI workflow optimization:

Step duration histograms. Track how long each step takes across many executions. Identify steps that are consistently slow or have high variance.

Token usage tracking. Record input and output token counts per model call. Identify prompts that are unnecessarily long and responses that are unnecessarily verbose.

Cost attribution. Assign costs to each workflow, each step, and each tool. Identify which workflows are expensive and where the cost concentrates.

Success rate tracking. Track per-step success rates. A step that fails 10% of the time is a reliability concern even if retries mask the failures.

These metrics, derived from structured logs, enable data-driven optimization of AI workflows. Without them, optimization is guesswork.

FAQ

How much does structured logging increase log volume?

Structured JSON logs are larger than unstructured text lines by approximately 2-3x due to field names and formatting. However, the reduction in debugging time and the ability to filter and search efficiently more than compensate for the storage cost.

Should I log model prompts in full?

Not in production. Full prompts contain context that may be sensitive (source code, user data) and are extremely verbose. Log prompt summaries (template name, key parameters) at INFO level and full prompts at TRACE level for debugging.

How do I handle log volume from highly concurrent AI systems?

Sampling reduces volume without losing signal. Log every ERROR and WARN. Sample INFO logs at 10-50% during normal operation. Log everything during incidents. Adaptive sampling increases fidelity when error rates rise.

What log format should I use for AI workflows?

JSON with the schema described above. JSON is universally parseable by log aggregation systems, AI models, and human readers (with formatting). Avoid custom text formats that require custom parsers.

Sources

• Structured Logging Best Practices - Google Cloud
• OpenTelemetry Logging - OpenTelemetry Project
• 12-Factor App Logging - Heroku
• Observability Engineering - O'Reilly

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