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Demand prediction
● Active v23Owner: Maria Chen · Supply Chain VP · 6 integrations connected · open agent UI →
Connected
6 / 6
3 sources · 3 outputs
Sync health · 24h
100%
no failed syncs
Data ingested · 7d
2.4M rows
SAP sales · NOAA weather · Snowflake
Output events · 7d
412
alerts + tickets + digests
Data sources
3 connected · what the agent reads from
SAP
SAP ERP · Sales history
Historical weekly sales by SKU-store · 4 years backfill
NOAA
NOAA Weather API
Regional weather forecasts + anomaly indices
❄
Snowflake · SKU master
SKU catalog, store metadata, supplier lead times
Add a data source
Postgres, BigQuery, REST API, S3, Salesforce, Workday…
Postgres, BigQuery, REST API, S3, Salesforce, Workday…
Outputs
3 active · where the agent writes / notifies
Slack · #supply-chain-alerts
Markdown alerts above medium severity
Email · daily digest
9am PT digest to Maria Chen + 4 stakeholders
SNOW
ServiceNow · ticket queue
Open ticket on each high-severity markdown alert
Add an output
Webhook, Teams, PagerDuty, Jira, custom API…
Webhook, Teams, PagerDuty, Jira, custom API…
Each integration becomes a tool the agent can call (data sources are read tools; outputs are write tools). Tool calls are recorded in the trace and counted toward eval / regression scoring. Outputs are simulated in the sim environment — the agent can't actually post to Slack during eval runs.