Me and my coding partner have been experimenting with AI agents lately. We recently participated in a hackathon where we tried to build a system with multiple agents running at the same time. We hit some major roadblocks when trying to get them to work together properly.
We’re trying to figure out what components we would need to make this actually work. Our current thinking includes using some kind of persistent memory solution for the agents, maybe an orchestration framework to manage the workflow, and probably some monitoring tools to track what’s happening.
Has anyone here built something like this before? Is it even feasible with current technology? Would this type of system be useful for real projects?
honestly the hardest part isn’t the tech stack but getting the agents to not step on each other. we tried langchain agents but ended up rolling our own simple coordinator that just queues tasks. way less headache than fancy frameworks imho.
Yeah, multi-agent systems work great. I’ve built several at work over the past few years.
Biggest mistake? Teams jump straight into complex orchestration. Start with basic pub/sub first. We used RabbitMQ and it worked perfectly.
For memory, don’t overthink it. Shared database handles most cases fine. We tried fancy vector databases but went back to PostgreSQL with Redis caching.
Define clear agent responsibilities upfront - this really bit us. Each agent needs one specific job. Overlapping responsibilities create race conditions and conflicting actions.
You’ll spend more time on monitoring than expected. Build dashboards early because debugging distributed agents without visibility is hell.
Real world applications exist. We automate code reviews and deployment pipelines. Saves us 20 hours weekly.
Start with 2-3 agents max. Scale up once you’ve nailed the basics.
Multi-agent systems definitely work in production, but the learning curve’s steeper than you’d think. We deployed one for customer support automation six months ago and hit several gotchas that weren’t obvious upfront. The biggest pain was agent coordination conflicts. Multiple agents trying to modify shared resources at once creates race conditions that are a nightmare to debug. We fixed it with simple Redis distributed locks before any state changes. For tech stack - event-driven architecture beats request-response patterns for agent communication. Apache Kafka handled our message throughput great, though it needs more setup than lighter options. What surprised me most was how critical agent lifecycle management is. Agents crash, get stuck in infinite loops, or eat up resources like crazy. Build automatic restarts and resource limits into your orchestrator from day one - you’ll thank yourself later. For monitoring, we used OpenTelemetry tracing across all agents. That visibility was essential for diagnosing failed workflows or figuring out why things took forever. Bottom line: the system cut manual intervention by about 60% in our case, so it was worth the initial complexity.
Building multi-agent systems can be quite complex, and I learned this firsthand from a project I worked on last year. One major takeaway was the importance of not trying to create everything from scratch; leveraging existing frameworks like CrewAI can save a lot of time and reduce headaches with orchestration. Communication between agents can also become a significant challenge, so using a Redis message queue improved our inter-agent communication over direct API calls. Additionally, while persistent memory is vital, it’s crucial to manage what you store to avoid high costs. Implementing robust error handling is necessary as agents can fail in unpredictable ways. Lastly, comprehensive monitoring is indispensable, and we found that utilizing structured logging offered great insights into our agents’ decision-making processes. Overall, it’s an achievable goal with the right planning and infrastructure.