Trace model
Follow the transaction, not only the model call
Agentic payments cross planning, tools, policy, credentials, merchants, and rails. A model trace may show that an agent selected a seller, but it does not prove the order was authorized or settled. A processor trace may show a successful request, but it does not identify the user's original goal or whether the merchant delivered. Observability should center on an internal transaction identifier that links each subsystem's events while preserving their distinct responsibilities.
The trace begins with authenticated principal and intent references, not raw conversation by default. It includes candidate and selected order digests, mandate, policy decision, credential issuance, payment attempts, callbacks, verified rail state, fulfillment, reconciliation, and recourse. Each event records issuer, timestamp, environment, status, and correlation identifiers. Operators can then answer whether a failure arose in planning, authority, execution, settlement, or assurance rather than treating every incident as “the AI failed.”
The unit of observability is the economic action and its evidence chain—not a prompt, API request, or transaction hash in isolation.
Define a telemetry contract across services
Logs, metrics, and traces should share stable field names for transaction, principal reference, agent, tenant, intent, mandate, order, policy decision, credential reference, merchant, payment attempt, rail, fulfillment, and case. Store opaque references where direct identifiers are sensitive. Status fields should use documented enums, and timestamps should include time zone and event source. A free-form log message is useful for humans but unreliable as the only operational record.
Propagate correlation context through trusted headers or protocol fields without allowing callers to overwrite security-relevant identity. An external trace identifier can be recorded, but the receiving service should assign or validate its internal transaction mapping. Record request and response digests where payload comparison matters. HTTP Message Signatures can provide integrity for selected request components; telemetry should capture what was verified while keeping signatures, credentials, and personal data out of broad log indexes.
| Objective | Key signals | Question answered |
|---|---|---|
| Authority | Mandate, policy version, decision, reason code | Why was execution allowed or stopped? |
| Execution | Idempotency key, attempt, latency, external reference | Was the economic request sent once? |
| Settlement | Verified rail status, amount, asset, destination | What value movement did the rail record? |
| Assurance | Order, fulfillment, reconciliation, refund state | Did the principal receive the intended outcome? |
| Security | Signature result, key ID, revocation, anomaly | Was the actor and request trustworthy? |
Alert on control failure and unresolved money states
Availability alone is an incomplete objective. A fast service can duplicate charges, authorize outside a mandate, or lose the link between payment and order. Monitor policy bypass attempts, missing required evidence, stale decisions, duplicate idempotency keys with different payloads, repeated signatures or nonces, destination changes, payment states that remain unknown, settlement without fulfillment, refund without credit, and reconciliation sources that stop delivering expected data.
Thresholds should follow the actual use case, rail timing, and loss tolerance rather than invented universal benchmarks. Some signals warrant immediate containment even once, such as a production signing request without a decision identifier. Others require baselines by merchant, tenant, asset, or agent. Alerts need an owner, evidence-rich runbook link, and safe first action. If every anomaly pages the same team without transaction context, operators will learn to ignore the system before the important incident arrives.
- Measure allowed, denied, stepped-up, submitted, unknown, settled, fulfilled, refunded, and reconciled states separately.
- Alert when state combinations violate invariants, not simply when one API returns an error.
- Detect telemetry gaps: missing events, stalled source feeds, clock skew, and correlation failures.
- Include the transaction and decision references needed for investigation without including raw secrets.
- Exercise alerts with replayed fixtures and confirm that containment works when dependencies are degraded.
Design observability for privacy and incident reconstruction
Agent conversations can contain personal preferences, credentials, confidential business needs, and unrelated content. Payment payloads can contain regulated or highly sensitive data. Collect only fields needed for control and diagnosis, redact at ingestion, tokenize identities, restrict high-detail traces, and set retention by purpose. PCI DSS requirements apply when cardholder data enters scope, while other rails still need explicit access, encryption, audit, and deletion policies.
During an incident, preserve the evidence graph before rotating or deleting affected components. Investigators need the identity and key history, mandate and policy versions, order digests, credential events, attempts, verified rail states, fulfillment, and operator actions. Maintain clock synchronization and original event timestamps, distinguish observation from inference, and record corrections. NIST's risk-management approach supports this system-level view: monitoring should connect detection, impact assessment, response, and governance rather than producing disconnected model and payment dashboards.
- Never log private keys, bearer tokens, full account data, or unredacted authorization headers.
- Restrict raw prompts and tool payloads; retain structured decision inputs wherever they are sufficient.
- Audit access to transaction traces, exports, and administrative search tools.
- Keep append-only incident annotations instead of editing original events to fit a later conclusion.
- Test reconstruction from user report, merchant order, and rail reference as three independent starting points.
Source discipline
Primary sources
Product status and protocol behavior are checked against maintainer documentation. Company sources establish what their organizations publish; they do not independently prove adoption or performance.