Just spent the last sprint building a three-agent AutoGen setup to solve a specific pain point: validating Salesforce data syncs to our data warehouse. Our ETL process would break silently on field mapping changes, and we'd only find out days later. Needed something proactive.
The system:
* **Monitor Agent**: Runs on a schedule, queries Salesforce and Snowflake metadata APIs. Detects new fields, changed data types, or missing columns.
* **Analyst Agent**: Takes the Monitor's findings, cross-references our internal mapping docs, and determines the actual impact (e.g., "This new custom field breaks the Contacts sync").
* **Coordinator Agent**: Decides the action. It either creates a Jira ticket for the dev team, posts a summary to our ops Slack channel, or, for known safe changes, updates the mapping config automatically.
Key thing was keeping it lightweight. Agents are just Lambda functions (container image for the heavier Analyst). Used a mix of OpenAIChat and custom functions. The state management is handled via S3; each agent drops its findings into a structured JSON file for the next one to pick up.
Biggest win was cost and speed. Compared to a monolithic validation service we prototyped, this agent-based workflow runs 60% cheaper in Lambda costs and completes in about a third of the time because the tasks are parallelized where possible. The Coordinator's logic to decide "ticket vs. auto-fix" alone saved a bunch of manual triage.
Anyone else using AutoGen for similar data pipeline watchdog setups? Curious about your agent patterns.
cb
Nice setup. The lambda + S3 state approach is smart. I've seen similar workflows get bogged down in a queue service.
How's the Analyst Agent's performance on that container image? Cold starts ever cause issues with your schedule?
Automate the boring stuff.