What Evidence Confirms an AI Agent Trade Was Actually Executed?

An AI trading agent can only be trusted when there is clear, immutable evidence that a trade left the market and changed the account balance. The primary proof consists of a signed order acknowledgment, a fill report that includes price, quantity, and venue timestamp, and an updated account state that reflects the new position or cash balance.

What are the essential pieces of execution evidence?

• ·Signed order acknowledgment from the venue confirming receipt and acceptance of the order.
• ·Fill report detailing executed quantity, price, execution venue, and a precise timestamp.
• ·Post‑trade account snapshot showing the updated position, cash, and margin requirements.
• ·Mutation identifier (for example, a unique order ID) that persists across acknowledgment, fill, and account update.

Each element must be stored in a tamper‑evident log. The acknowledgment is typically a cryptographic signature from the exchange, the fill report is a structured message that includes fees, and the account snapshot is retrieved from the normalized account interface.

How does market data support execution verification?

Market data must expose its source, timestamp, and freshness. By matching the fill timestamp to the market data snapshot at that moment, you can confirm that the reported price was actually available. If the data source is missing or the timestamp is stale, the fill cannot be trusted without additional corroboration.

• ·Source identifier (exchange or data provider) attached to each price tick.
• ·Timestamp with sub‑second precision to align with fill reports.
• ·Coverage flags indicating whether the instrument was actively quoted at the time.
• ·Warnings for delayed or missing data that could affect confidence.

When multiple feeds are available, cross‑checking timestamps reduces the risk of a single feed outage or latency spike.

What role do durable mutation IDs and error states play?

A durable mutation ID is a unique identifier that travels with the order from creation through acknowledgment, fill, and settlement. It allows systems to detect duplicate messages, lost updates, or out‑of‑order processing. Explicit error states such as REJECTED, TIMEOUT, PARTIAL_FILL are required because a timeout alone does not prove failure; the order may have executed after the timeout period.

• ·Persist the mutation ID in a write‑ahead log before sending the order.
• ·Record any error code returned by the venue alongside the ID.
• ·Treat a timeout as a pending state and query the venue for final status before assuming failure.

If the venue later returns a fill for a timed‑out request, the mutation ID links the fill back to the original order, ensuring accurate accounting.

How should reconciliation be performed?

Reconciliation compares the expected account state derived from the execution evidence with the actual state reported by the account interface. Discrepancies can arise from delayed fills, partial executions, or system bugs. A robust reconciliation loop runs after each trade and flags any mismatch for manual review.

1. 01Capture pre‑trade account snapshot (positions, cash, margin).
2. 02Apply the execution evidence (price, quantity, fees) to compute the expected post‑trade snapshot.
3. 03Fetch the actual post‑trade snapshot from the account interface.
4. 04Log any differences and trigger an alert if the variance exceeds a defined tolerance.

The tolerance should be defined in owner‑signed limits so that only material deviations generate alerts.

What are common sources of uncertainty and how can they be mitigated?

Even with thorough evidence, uncertainty remains. Market data may be delayed, venues can experience partial fills that are reported later, and network partitions can cause duplicate acknowledgments. Mitigation strategies include redundant data feeds, periodic permission reviews, and an emergency stop that revokes the agent key while preserving existing positions for later review.

• ·Use multiple data providers to cross‑verify timestamps.
• ·Implement a retry‑on‑timeout policy that re‑queries the venue before marking an order as failed.
• ·Schedule regular reviews of agent permissions and limits to ensure they remain appropriate.

An emergency stop should cancel new order submissions and revoke the active agent key, but it does not automatically close open positions. Those require explicit owner review.

Further reading

• ·Felix documentation for detailed API contracts and error codes.
• ·What Order States Should an AI Trading Agent Understand?
• ·How an AI Agent Should Respond When an Order Request Times Out