If Your AI System Has No Receipts, It Has No Memory
The team cannot reconstruct why a decision was made, which model produced it, what it cost, or whether the output helped. Every new incident becomes an argument from memory.
The recognizable symptom
A successful result becomes a screenshot. A miss becomes a vague story. When someone asks what happened between the prompt and the outcome, the system has nothing durable to show.
The mechanism underneath
The product treats observability as infrastructure rather than part of the user-facing promise. Logs exist somewhere, but they are not tied to a claim, a decision, a human review, or a measured outcome.
What we built to contain it
We make the artifact part of the thesis: frozen protocols, explicit priors, cost receipts, outcome logs, and independent checks. The receipt is not bureaucracy added after the fact. It is how the system earns the right to become more autonomous.
Non-blocking receipt pipeline: sidecar pattern for retrieval, judgment, and logging
The main response path should only run inference and return. Everything else moves to a sidecar worker that consumes from a durable queue. Concretely: in a FastAPI endpoint, after generating the response, push a job to Redis Streams with a unique request_id, the prompt, the raw output, and a timestamp. The endpoint returns immediately. A separate worker (e.g., a Python process using redis-py) reads the stream, performs retrieval of source evidence, computes a judgment score (e.g., a calibrated confidence), and writes a receipt document to an append-only store (like a JSONL file or a database table). The receipt includes the request_id, the evidence snippets, the judgment, and a hash of the response for tamper-evidence. The worker acknowledges the message only after the receipt is durably written, enabling at-least-once delivery. To avoid duplicate receipts, the store uses the request_id as a unique key and the worker upserts. This pattern keeps the p95 latency of the main path unchanged because no I/O beyond the queue write occurs. A worked calculation: if the queue write takes 2ms and the worker takes 200ms, the user sees only the 2ms overhead. The limitation: receipts are eventually consistent—if the worker crashes before ack and the queue is not configured with a dead-letter policy, the receipt may be lost. Durable queues with retries mitigate this but add operational complexity.
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