Hi everyone! I’ve been experimenting with multi-agent setups and wanted to get some feedback on what’s been working for others.
I’m particularly interested in:
Agent selection - Are you manually specifying which agent to use in your requests, or letting the system choose automatically? I’ve found that sometimes the wrong agent gets picked unless I’m very explicit about it.
Tool permissions - Do you grant the same tool access to all your agents, or do you restrict certain tools to specific agents? Curious if this restriction actually improves performance.
I know results probably vary based on use case, but I’d really appreciate hearing about your experiences and any patterns you’ve discovered that work well!
Been running multi-agent systems in production for 18 months - learned some hard lessons. For agent selection, hybrid approach works best. Started with full automation but it picks poorly when context gets ambiguous. Now I use semantic routing based on input content plus fallback hierarchy. System tries auto-selection first, but if confidence scores drop below threshold, defaults to a generalist agent that delegates properly. Tool permissions - definitely restrict them. Had a costly incident where an agent with broad access started making API calls a specialized integration agent should’ve handled. Now each agent has defined scope - data agents only query specific databases, reporting agents get read-only access to outputs, external API agents are completely sandboxed. One thing nobody mentions much is message passing protocols. Without proper inter-agent communication standards, you get chaos. I implemented simple pub-sub pattern where agents announce their actions and results. Cut duplicate work significantly and improved overall system coherence.
I’ve been working with multi-agent systems for 3 years and made plenty of mistakes.
Agent selection: I ditched pure auto-selection after it burned me too many times. Now I use a routing layer with intent classification first, then manual override when confidence drops. Way better than blind guessing.
Tool permissions matter. Found this out when an agent started hammering expensive APIs it shouldn’t touch. Now I map tools to roles strictly - database agents get read/write, analysis agents get read-only, communication agents can’t touch data.
Biggest win was adding agent state management. Without it, agents constantly repeated work or contradicted each other.
This Google video helped me fix tons of coordination issues:
Last tip - log everything between agents. You’ll need those logs when things break, and they will break.
After two years building multi-agent workflows, I’ve learned domain-based orchestration beats pure routing every time. Don’t try picking the perfect agent upfront - segment by problem domain and let agents handle handoffs themselves. Way better than centralized selection. For tools, learned this the hard way: compartmentalize but don’t go overboard with restrictions. I give each agent base tools plus domain-specific ones. Database agents get full CRUD but only for their schemas. Analysis agents can read everything but only write to temp storage. Here’s what nobody talks about - error recovery. Multi-agent systems break differently than single agents. I now use circuit breakers between agents with graceful degradation paths. When one agent crashes, the system routes around it instead of everything falling apart. Monitoring agent interactions matters as much as individual performance. Tracking token usage patterns between agents showed me bottlenecks I’d never have spotted otherwise.
Two things make or break multi-agent systems - token management and agent memory.
Your agent selection question hits something I deal with daily. Pure automation fails at scale because models can’t read context nuances under enterprise load. I run a confidence threshold system now - anything below 75% gets routed to a specialized dispatcher agent that understands the business logic.
For tool permissions, think security first. Learned this when one agent started accessing customer PII it shouldn’t touch. Now I use capability-based access where agents earn tool permissions based on reliability scores.
Here’s what really moved the needle - agent memory architecture. Most people focus on routing and permissions but ignore that agents forget everything between conversations. I implemented persistent context stores where agents write and read shared memory.
Game changer was adding agent performance metrics. Track success rates per agent type and adjust routing weights dynamically. My analysis agents now handle 40% more requests because the system learned they’re more reliable than generalists.
One more thing - test failure scenarios early. Multi-agent systems fail in weird cascade patterns. I simulate agent timeouts and memory corruption regularly to ensure graceful degradation works.
What scale are you running at? The patterns change dramatically once you hit certain volume thresholds.
coordination overhead kills most multi-agent setups. you can get fancy with routing and permissions, but if agents don’t know what others are doing, you’ll waste compute like crazy. i switched to shared context pools - agents peek at ongoing work before starting new tasks. saves tons of redundant processing and keeps everything aligned without complex orchestration.
