Session Replay Log

The chronological backbone of the log. This is an immutable, append-only sequence of all discrete occurrences during the session. Each event is a structured record with a high-resolution timestamp, a unique identifier, and a type (e.g., user_input, tool_call_initiated, llm_response, state_update). This stream enables deterministic replay by providing the exact order of operations.

• Example Events: { "timestamp": "2024-01-15T10:30:00.123456Z", "event_id": "evt_abc123", "type": "tool_call", "payload": { "tool": "get_weather_api", "parameters": {"city": "London"} } }

Periodic, full captures of the agent's internal memory and context. Unlike the event stream which records changes (deltas), a state snapshot is a point-in-time record of the agent's complete working memory, including its conversation history, retrieved context from a vector database, plan steps, and any internal variables. These snapshots are essential for forensic state reconstruction, allowing an auditor to restore the agent's exact "mindset" at any moment, independent of the event replay path.

Data that answers "why" an action was taken. For every logged action (e.g., an API call, a message sent), this component captures the causal chain that led to it. This includes:

• The specific user prompt or system instruction that triggered the session.
• The retrieved context (e.g., document IDs, knowledge graph nodes) that informed the decision.
• The internal reasoning steps (planning, reflection) that preceded the action.
• The policy or guardrail that was evaluated and its pass/fail result.

This metadata is critical for explainability and compliance verification, creating an intent-action mapping.

Quantitative measurements interleaved with the event stream. This data provides the operational and economic context for the agent's behavior. Key metrics include:

• Latency: Breakdowns for LLM calls, tool execution, and total response time.
• Cost Attribution: Token counts for prompts and completions, costs of external API calls.
• Resource Usage: Memory consumption, CPU utilization of the agent runtime.
• Success/Failure Flags: Outcomes of tool calls, context retrieval hits/misses, and policy evaluations.

This component transforms the log from a behavioral record into a tool for performance benchmarking and cost telemetry.

The cryptographic safeguards that make the log a trustworthy audit trail. This is not a separate data stream but a set of verifications applied to the other components. It ensures non-repudiation and tamper-evidence.

• Cryptographic Hashing: Each log entry includes a hash of the previous entry, creating a hash chain. Altering any past entry breaks the chain.
• Digital Signatures: Critical entries (like final actions or state commits) are signed by the agent's secure module or a trusted authority, providing telemetry attestation.
• Secure Timestamping: Timestamps are optionally signed by a trusted time-stamping authority, providing tamper-proof timestamping for legal admissibility.

Pointers to related systems and artifacts outside the log itself. A session does not occur in a vacuum; the agent interacts with external state. This component provides traceability to:

• Tool & API Payloads/Responses: References to full request/response bodies stored in a separate, secure log (e.g., an API gateway log).
• Data Source Versions: Commit hashes of knowledge graphs, model IDs and versions of LLMs used, snapshot IDs of vector database collections.
• Orchestration Context: Correlation IDs that link this agent's session to a broader workflow or multi-agent interaction graph.

These links complete the provenance chain, allowing auditors to follow the data trail beyond the agent's runtime.

An immutable, chronological record of all actions, decisions, and state changes performed by an autonomous agent. Unlike a basic log, it is structured for compliance verification and forensic analysis.

• Key Feature: Designed as legal evidence, often meeting standards like GDPR or HIPAA.
• Contrast with Session Replay: An Audit Trail is the authoritative source record; a Session Replay Log is a high-fidelity, replayable derivation used for debugging and visualization.

An architectural pattern where an agent's current state is not stored directly, but is derived by replaying an immutable, append-only log of all state-changing events it has processed.

• Core Mechanism: The event log becomes the single source of truth. State is a function of the event history.
• Benefit for Auditing: Enables perfect forensic state reconstruction at any historical point by replaying events up to that moment, aligning directly with the goals of a Session Replay Log.

A directed graph data structure that explicitly models the cause-and-effect relationships between an agent's observations, internal reasoning states, decisions, and executed actions.

• Purpose: Moves beyond a sequential log to capture the 'why' behind actions. Each node is a state or action; edges represent causal links.
• Analytical Value: Enables root-cause analysis by tracing backward from an outcome to the initiating observation or decision, providing deeper insight than a linear replay.

A logging standard that provides cryptographic proof of an action's origin and integrity, preventing the acting agent or system from later denying its involvement.

• Technical Implementation: Uses digital signatures from a secure hardware module or trusted identity to sign each log entry.
• Critical for Compliance: Essential for meeting regulatory requirements where accountability must be irrefutable. A Session Replay Log built with non-repudiation provides the highest level of audit assurance.

The systematic recording of each discrete logical inference, planning operation, or reflection cycle an agent performs en route to a final decision or action.

• Scope: Captures the internal cognitive process, not just inputs and outputs. This includes chain-of-thought, tree-of-thought, or reflection steps.
• Link to Replay: This data is the critical 'intermediate state' that allows a Session Replay Log to reconstruct not just what the agent did, but how it decided to do it.

The investigative technique of constructing and analyzing a unified chronological timeline from disparate audit logs and Session Replay Logs to understand the sequence and root cause of an agent incident.

• Process: Correlates logs from the agent, its tools, and the surrounding system to create a single view of the incident.
• Outcome: Answers critical post-mortem questions by using replay data to isolate the exact decision or external event that triggered a failure or policy violation.