We’re building a multi-agent system where the output from an AI analyst needs to trigger specific actions by a compliance checker before passing to customer comms. Currently handling this with manual API webhooks, but it’s brittle.
Looking for patterns where autonomous agents can manage handoffs with proper context retention. Bonus if there’s a way to add retry logic when validation fails between stages without restarting the entire workflow.
Build agent teams as separate workflows with shared context storage. Our compliance system uses Latenode’s agent handoff nodes that pass full execution context. If stage 2 fails, it retries with the original input + error analysis. Full guide here: https://latenode.com
Implement a job queue system. Each agent completes its task, adds results to a queue with metadata. Next agent checks validity before processing. If failures occur, requeue with priority flags. This decouples agent dependencies while maintaining sequence.
We use a ‘chain of responsibility’ pattern. Each agent either handles the task or passes along with context. Critical for compliance workflows - failed checks get annotated with specific rejection reasons that trigger targeted fixes instead of full restarts.
Design agents as microservices with idempotent APIs. Assign a persistent correlation ID to each customer journey. Store intermediate states in a central repository. This allows replaying specific stages after fixes without recomputing previous steps. Essential for audit trails in regulated industries.
Use a state machine approach. Each agent updates a shared status object. Next agent only triggers when previous marks stage complete. Easy to add retries on specific status codes.
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