Agentic Workflow Automation

Market-data teams watch many feeds, and most days nothing is wrong — which is exactly the problem. A monitor that pages a human for every holiday gap trains people to ignore it; one that silently swallows a decimal-shifted price is worse. The fashionable answer is 'put an agent on it'. The engineering answer, built here as feed-health-pipeline: detect anomalies mechanically, spend judgment — machine or human — only on real exceptions, and never let an uncertain judgment pass silently.

Seven deliberately heterogeneous public feeds (four formats, four cadences) flow through payload and schema guards, format-specific parsers and five deterministic checks — freshness, gaps, spikes, bounds, duplicates — with every threshold in config, not code. When all checks pass, the run costs zero LLM calls. Only when one fails does a bounded tool-use agent investigate that single incident: it can read the feed spec, recent observations and the previous snapshot, gets a hard six-turn limit, and must submit exactly one classified verdict with its full tool trace saved.

The verdict then hits a gate written in code, not vibes: confidence below 0.8, any consequential action, or any unknown classification routes to an append-only human review queue. The staged-failure showcase proves the design point — both wrong verdicts arrived with low confidence, were caught by the gate, and were corrected on the record with dated notes. Not a single incident was dropped, and nothing wrong was ever confident.

A scenario builder deterministically tampers the committed baseline snapshots into six failure cases — byte-identical on every rebuild — and each was run against the live agent, with the full artifacts committed. Result: 4 correct, 2 wrong-but-gated, 0 wrong-and-confident. Both misses carried low confidence, landed in the review queue, and were corrected by a human with dated notes.

REAL RUN DATA — results/demo/<scenario>/triage/*.json · claude-sonnet-5

The standout result. The same spike check fired for two very different events: a ×10 decimal shift and a genuine −30% market move. A threshold cannot tell them apart — that is exactly the rare, unstructured judgment the agent exists for.

The agent distinguished them on provenance grounds — comparing against the rotated previous snapshot through its tools — not on headline size.

A verdict is auto-logged only when it is confident and its action is safe. Everything else routes to an append-only human review queue — entries are never deleted, and resolving one records a dated note, so wrong verdicts stay on the record next to their correction.

Fail-safe by construction: the pipeline never requires the LLM to run, and never silently drops an incident.

On the first live run the freshness check flagged euro-area HICP as 189 days stale. Not a bug: Eurostat had genuinely frozen the dataflow after a methodology change. The feed was retired, replaced by the EA21 unemployment series, and every threshold was recalibrated against observed history — all documented in the repo.

Public endpoints changing and dying is treated as a feature of the demo, not a footnote: the guards exist precisely because “the fetch succeeded” does not mean “the data is fine.”

Deliberately heterogeneous — four formats, four cadences, seven independent public endpoints — so the parsers, payload/schema guards and per-feed thresholds all earn their keep. Every threshold lives in config, not code: a wrong limit is a visible, fixable config change, not a bug.