Landscape
Where Decision Receipt sits in the AI accountability stack
Decision Receipt addresses a narrower layer than observability, guardrails, logging, governance, code quality, or explainability: what policy evaluation was recorded for this proposed action, and can a relying party check that record offline?
Category by category
What each tool does — and the gap it leaves
LLM Observability — LangSmith, Arize, Weights & Biases
What they do well
Track prompts, completions, token usage, latency, and model behavior over time — essential for debugging and performance tuning.
The gap Decision Receipt fills
Observability platforms record model and system behavior. Decision Receipt addresses a different layer: it binds a proposed action to the recorded policy evaluation and emits a signed record before execution.
AI Guardrails — Guardrails AI, NeMo Guardrails
What they do well
Filter, validate, and constrain model outputs — catching toxic content, hallucinations, and schema violations before they reach the user.
The gap Decision Receipt fills
Guardrails operate on output quality, not decision authority. A guardrail can confirm an output is well-formed and safe; it cannot confirm the decision behind it was evaluated against competing hypotheses, grounded in provenance-validated evidence, and approved under an explicit policy posture.
Audit Logging — Splunk, Elastic, Datadog
What they do well
Capture, index, and search operational events at scale — providing after-the-fact visibility into what happened and when.
The gap Decision Receipt fills
Audit logs record events. A Decision Receipt binds a proposed action to recorded inputs, a policy version, and an evaluation result. It does not establish that the result was correct.
AI Governance Platforms — IBM OpenPages, ServiceNow AI Governance
What they do well
Document policies, risk frameworks, model inventories, and compliance workflows — giving organizations a structured record of their AI governance posture.
The gap Decision Receipt fills
Governance platforms define and document policy. They do not enforce it at the moment a decision is made. Decision Receipt evaluates policy rules at decision time, as the events occur, and records a deny-by-default verdict when evidence is insufficient or policy is unmet. Governance becomes a checkable runtime record, not a documentation exercise.
CI / Code Review Tools — GitHub Actions, SonarQube
What they do well
Validate code quality, run test suites, enforce branch policies, and gate deployments on passing checks.
The gap Decision Receipt fills
CI tools verify that code is correct. They do not verify that a runtime decision made by that code is authorized. Decision Receipt operates at decision time, not build time — evaluating the specific evidence, context, and policy state present when an autonomous system attempts to act.
Explainability Tools — LIME, SHAP
What they do well
Generate post-hoc explanations of model behavior — attributing predictions to input features to help humans understand why a model produced a given output.
The gap Decision Receipt fills
Explainability tools generate rationales about model behavior. Decision Receipt records the evidence digest, policy evaluation, and replay result so another party can compare a re-run against the recorded result.
Comparison matrix
Eight capabilities across seven tools
| Capability | Decision Receipt | LLM Observability | Guardrails | Audit Logs | Governance | CI / Code Review | Explainability |
|---|---|---|---|---|---|---|---|
| Cryptographic signing of decisions | Yes | No | No | No | No | No | No |
| Deterministic replay from sealed evidence | Yes | No | No | No | No | No | No |
| Competing hypothesis enforcement | Yes | No | No | No | No | No | Partial |
| Policy evaluation at decision time | Yes | No | Partial | No | No | Partial | No |
| Portable signed evaluation record | Yes | No | No | No | No | No | No |
| Offline verification with pinned keys | Yes | No | No | No | No | No | No |
| Deny-by-default posture | Yes | No | Partial | No | No | Partial | No |
| Evidence provenance chain | Yes | No | No | No | Partial | No | No |
Summary
A new layer, not a replacement
Decision Receipt is not a replacement for observability, guardrails, logging, governance, CI, or explainability. It adds a portable, signed record binding a proposed action to its recorded policy evaluation. It does not assert that the underlying action executed.
For autonomous AI systems operating in regulated or high-consequence environments, the question is not whether you observed the decision, filtered the output, or logged the event. The question is whether you can prove the decision was authorized. That is what Decision Receipt does.
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