Log Schema

The foundational header data that establishes the context and provenance of a tool call. This is the "who, when, and where" of the log entry.

• Timestamp: Precise, high-resolution time (e.g., ISO 8601 with nanoseconds) of the call initiation.
• Trace/Span IDs: Unique identifiers from distributed tracing systems (e.g., OpenTelemetry) to correlate this call with a broader user request or workflow.
• Session ID: A stable identifier for the AI agent's interaction session.
• Agent ID/Version: The specific agent or model that initiated the call.
• User/Principal ID: The authenticated human or system user on whose behalf the agent is acting.

Records the declarative specification of the tool being called and the agent's reasoning for selecting it. This provides auditability into the agent's decision-making process.

• Tool Name & Version: The unique identifier and version of the invoked function or API.
• Tool Description: The natural language description of the tool's purpose, as defined in its schema (e.g., OpenAPI).
• Selected Parameters: The specific arguments the agent chose to pass, before any validation or execution.
• Reasoning/Thought Process: An optional field capturing the agent's chain-of-thought or justification for selecting this tool with these parameters, crucial for debugging agentic behavior.

Captures the exact outbound request sent to the external system. This is the immutable record of the action attempted, essential for non-repudiation and replay.

• Full HTTP/API Request: Includes the final URL endpoint, HTTP method (GET, POST), headers (with sensitive values redacted), and the complete request body.
• Resolved Parameters: The final, fully resolved parameter values after any templating or variable substitution by the orchestration layer.
• Credential Scope: An identifier for the set of permissions or API keys used (not the secret itself), linking to a secure credential management system.

Documents the system's response and the interpreted outcome. This is critical for validating execution correctness and diagnosing failures.

• Raw HTTP/API Response: Includes status code, response headers, and the full response body (truncated or hashed if excessively large).
• Response Time/Latency: Duration from request dispatch to final byte received.
• Success/Failure Classification: A normalized field indicating if the call succeeded, failed transiently, or failed permanently.
• Structured Output: The data extracted and parsed from the response by the agent or orchestration layer, often validated against a Pydantic or JSON Schema model.

Enriches the log with security signals and regulatory metadata. This transforms operational data into forensic evidence.

• Authorization Context: The roles, scopes, and permissions evaluated to authorize this specific tool call.
• Data Classification Tags: Labels indicating the sensitivity of data in the request/response (e.g., PII, PCI, PHI).
• PII Redaction Flags: Boolean indicators confirming that personally identifiable information was algorithmically redacted from the logged payloads.
• Regulation ID: Links to applicable compliance frameworks (e.g., GDPR:Art-30, HIPAA:164.312).
• Tamper-Evident Seal: A cryptographic hash (e.g., SHA-256) of the log entry, enabling later verification of integrity.

Captures the broader workflow state and system health at the moment of execution. This is key for root cause analysis during complex, multi-step agentic failures.

• Workflow Step/Iteration: The position of this call within a larger plan (e.g., "Step 3 of 5", "Refinement loop 2").
• Preceding & Sibling Calls: References to trace IDs of directly related tool calls in the same session.
• System Metrics: Snapshot data like host CPU/memory, model latency, or queue depth at the time of the call.
• Error Handling Actions: Records of automatic retries, fallback mechanisms, or circuit breaker states triggered by this invocation.

The practice of writing log messages as machine-readable, key-value pairs (e.g., JSON) instead of unstructured plain text. This is the implementation of a log schema, enabling automated parsing, filtering, and analysis.

• Key Benefit: Enables precise querying (e.g., WHERE tool_name='process_payment' AND status='ERROR').
• Standard Format: JSON is the de facto standard, allowing nested objects and arrays to represent complex events like a tool call with its parameters and nested results.

A write-once, append-only data store where entries cannot be altered, overwritten, or deleted after creation. This is a critical security property for audit logs derived from a schema.

• Integrity Guarantee: Provides verifiable evidence that log history has not been tampered with, essential for forensics and non-repudiation.
• Implementation: Often uses Write-Once Read-Many (WORM) storage or cryptographic techniques like hash chaining (e.g., Merkle Trees) to make alterations evident.

A chronological, immutable record of all significant events and actions (e.g., tool invocations, user logins, configuration changes) within a system. A log schema defines the structure of each entry in this trail.

• Purpose: Provides a verifiable history for security incident investigation, regulatory compliance (e.g., SOX, HIPAA), and operational debugging.
• Scope: More comprehensive than simple error logs; includes who (identity), what (action), when (timestamp), where (source), and outcome for every auditable event.

The process of collecting, centralizing, and indexing log data from multiple disparate sources (servers, containers, applications, agents) into a single platform. A consistent log schema is prerequisite for effective aggregation.

• Analytical Power: Enables unified search, correlation, and dashboarding across an entire system. For example, correlating a failed API call from an AI agent with a backend service error.
• Tools: Implemented by platforms like the Elastic Stack (ELK), Datadog, Splunk, and Grafana Loki.

A software architecture pattern where the state of an application is determined not by a current database record, but by a sequence of immutable events stored in an append-only log. The event log is the system of record.

• Relationship to Logging: This pattern treats the audit trail as the primary data model. Each tool invocation by an AI agent would be an event stored according to a strict schema.
• Benefit: Enables log replay to reconstruct past system states and provides a perfect audit history by design.