Action Provenance

The foundational data store for action provenance. It is a write-once, append-only sequence where each agent action is recorded as a cryptographically-secured entry. This creates a tamper-evident history, as any alteration to a past entry would break the chain of cryptographic hashes (e.g., in a Merkle Tree structure). Its primary purpose is to provide a single source of truth for forensic state reconstruction.

A directed graph data model that explicitly captures the cause-and-effect relationships within an agent's execution. Nodes represent entities like observations, internal states, decisions, and executed actions. Edges represent the causal links between them (e.g., 'Observation X triggered Decision Y, which led to Action Z'). This moves beyond a simple chronological log to provide a semantic map of reasoning, essential for debugging complex behaviors and justifying decisions.

The atomic unit of provenance. It is a cryptographically-signed data structure that bundles an action with its essential context to create a standalone proof. A complete record includes:

• The action's content and parameters.
• A tamper-proof timestamp.
• The agent's identity or session ID.
• References to the prior state and inputs that caused it.
• A digital signature from a trusted module. This structure enables non-repudiation, preventing the agent or system from later denying the action's origin.

The component that links low-level agent operations back to high-level goals. It explicitly logs the user instruction, business requirement, or triggered event (the intent) that initiated a chain of agent behavior. This mapping is critical for auditability, as it answers the 'why' behind an action. It is often implemented as part of a traceability matrix, connecting business objectives to specific API calls, data retrievals, and state changes performed by the agent.

The systematic recording of an agent's internal cognitive process. This goes beyond logging final outputs to capture each discrete logical inference, planning operation, and reflection cycle. For example, in a ReAct (Reasoning + Acting) loop, this would log each 'Thought:' step generated by the LLM before an 'Action:' is taken. This granular telemetry is the raw material for agent reasoning traceability, allowing engineers to visualize and validate the agent's decision-making pathway.

The complete, end-to-end lineage of data and decisions. It represents an unbroken, verifiable sequence that documents the full lifecycle: from initial user input and retrieved context, through the agent's internal processing and state transitions, to the final executed action and any generated output. A robust provenance chain integrates the other components (ledger, graph, records) to provide a holistic view, enabling cross-session auditing and compliance with regulations like the EU AI Act that require detailed record-keeping.