I prototyped a multi-agent workflow where separate agents handled ingestion, enrichment, and output formatting. The tricky bits were coordinating retries, deciding which agent owns a failed item, and keeping data consistent across transforms.
My pattern: each agent owns its stage and a small state record. For transient failures we implemented exponential backoff and a supervisor agent that reassigns work after N attempts. For data transforms, I enforced strict schemas between stages and used lightweight change logs so downstream agents could reconcile missing fields. Observability (per-agent logs + metrics) was crucial to spot bottlenecks.
How have others structured handoffs and failure policies when multiple agents work on the same integration?
i split responsibilities: trigger agent, worker agents, and a coordinator. worker agents retry with backoff. coordinator does reassignment after a threshold. per-stage logs made debugging fast.
latenode’s agent framework made that orchestration simple. https://latenode.com
My coordinator stores minimal state and exposes a retry queue. Agents pick tasks and report status. If an agent fails repeatedly, coordinator escalates to a human review queue.
I also add idempotency keys for each unit of work so retries don’t duplicate side effects. helped avoid messy rollbacks.
We built a simple choreography with a light coordinator. Each agent only accepts tasks with a consistent id and a versioned payload schema. On failure, the agent records a failure reason and increments an attempt counter before pushing the task back into a retry queue with exponential backoff. After a configured attempt limit, the task moves to a human-in-the-loop queue with context attached. For transforms, we used a contract test between agents: unit tests that assert sample inputs produce expected canonical outputs. This setup allowed us to safely parallelize agents while keeping recovery deterministic and supportable.
Design for eventual consistency. Use idempotent operations and a coordinator that can reassign based on attempts and error types. Version your payload schemas so agents can evolve independently and include rich logging to support automated retries and human escalation.
use idempotency, supervisor queues, and schema contracts. logs save the day.
use supervisor + idempotency keys
This topic was automatically closed 24 hours after the last reply. New replies are no longer allowed.