How are you using multi-agent AI systems in your daily workflow?

How well is your agent performing?

It sounds complicated. Is your Agent trying to solve everything?

I've set a fully async patern. blobs chunked into sqlite shards.

It's a blind fire n forget go worker danse.

wich can be hold as monitoreed or scale as multiple instances if needed by simple parameters.

Basicaly, It's a job as librairy patern.

If you dont need real time, its bulletproof and very llm friendly.

and a good token saver by the batching abilities.

More interestingly, what frameworks/harnesses/architecture are people using to drive multi-agent workflows?

On our team we split the flow into six agents: scraper, classifier, context builder, summary writer, responder, and post-monitor. They never share a conversation; each pulls jobs tagged for it from a Postgres queue, locks the row with `SELECT ... FOR UPDATE`, hits a shared vector store for context, writes the result, and lets the orchestrator (n8n flow) enqueue the next job. We keep the prompts tiny and deterministic, so the only state is the job row and the vector hash. This async job-as-library + policy layer is the only architecture that scales; the thing that fails spectacularly is when we let them all talk on a single Slack channel, because they start racing to be decision-maker and race for tool calls. The trick was to treat every tool as a service call with capacity controls plus a watcher that unpicks deadlocks.

What fails spectacularly in our setup: agents that share a conversation thread and try to resolve conflicts in real time. They race to add the last word, produce verbose non-decisions, and eventually one agent just agrees with whatever was said last. Consensus is a bad protocol for async, unequal agents.

What works: role clarity + veto rights. One agent can only block, never propose. One agent makes calls, others can raise flags. You stop the chatbot parliament problem and actually get decisions.

The other pattern worth stealing from production systems: treat inbound events (emails, webhooks, form submissions) as the task boundary, not the conversation turn. An agent that owns a mailbox and processes messages one at a time is dramatically more auditable than one that's always-on and decides what to react to. You can replay it, diff its outputs, and understand why it did what it did.

I juggle multi-agents for persistent tasks like coding and debugging. Makes context-switching a breeze. How’ve you optimized yours?