Our team has been working with AI agents for a while now and we keep running into problems when trying to get them to work smoothly in production.
We use LangChain and LangSmith which helps a lot, but the tooling for monitoring and deploying agents still seems pretty limited. I want to know what strategies everyone else is using:
Single agent deployment: How do you handle putting complex agents live? For us it feels like shipping one huge application and the costs add up fast when we need to scale.
Multi-agent setups: Anyone running agents across different servers or containers? We tried this but it created new headaches.
Agent communication: This is where we struggle most. Getting agents to talk to each other reliably is hard. We tried to create standards but different teams keep changing things and breaking stuff.
Request tracking: What do you use to follow requests that go through multiple agents? We tested LangSmith and OpenTelemetry but nothing feels like it was made specifically for agents.
Other issues: What other problems do you face with production agent systems?
I think a lot of these problems exist because everything is moving so fast, but we still need better tools.
I’m asking because I want to understand how others handle these challenges and maybe find better solutions. Any advice would be awesome.
Your pain points hit close to home. We went through the same struggles last year before figuring out what works.
Biggest game-changer was ditching service-to-service orchestration for event-driven architecture. Each agent publishes results to specific topics and grabs what it needs. No direct dependencies, so one failing agent won’t kill everything.
For deployment, we containerized individual agent types but run multiple instances behind load balancers. Scale up the bottleneck agents, keep lightweight ones minimal. Way better than monolithic deployments.
Request tracing? Correlation IDs that flow through every agent interaction. Custom middleware logs entry/exit points with the same ID. Simple but works great for debugging multi-hop requests.
Here’s what nobody mentions - agent timeouts and retries. Set aggressive timeouts with exponential backoff. Agents that hang will wreck your system faster than ones that fail fast.
We also separate agent logic from runtime. Same code runs everywhere, but config changes behavior. Makes testing way more predictable.
The tooling gap is real, but focus on observability and fault tolerance patterns. Most production issues are just cascading failures and resource exhaustion anyway.
Been there with the production nightmares. Our breakthrough was treating agents like stateless functions behind a gateway. We serialize all agent state to Redis, so any instance can pick up where another left off. Solved both scaling and reliability since agents become completely interchangeable. For multi-agent coordination, we ditched direct communication entirely. Everything goes through a central event bus with schema validation. Agents publish events and subscribe to what they need - way cleaner than managing point-to-point connections. Biggest lesson: implement proper graceful degradation. When Agent B goes down, Agent A should keep working with reduced capabilities instead of failing completely. We define fallback behaviors for every integration point. Cost control came through batching. Agents process multiple requests together when possible rather than spinning up for each task. Made a huge difference in our AWS bills. Honestly, tooling will catch up eventually, but these patterns help regardless of framework.
we got burned too many times and went back to simpler setups. kubernetes helped, but circuit breakers between agents were the real game changer - no more cascading failures when one goes down. switched from direct api calls to async msg queues too. it’s a bit slower but way more reliable.
I’ve dealt with these exact headaches for years. Patching together different deployment tools just creates more mess.
Switching to a workflow automation platform changed everything for me. Instead of wrestling with containers and protocols, I build agent systems as automated workflows.
Single agents scale properly without infrastructure overhead. Multi-agent setups become workflow steps that talk through defined data flows. No more broken standards or team fights.
Request tracking fixes itself - every execution gets tracked end-to-end automatically. You see exactly where things break and can replay failed runs.
I stopped fighting deployment tools and monitoring systems. Everything runs in one place with built-in observability. My team went from weeks on infrastructure to focusing on actual agent logic.
The key insight: AI agents are just specialized workflow steps. Think about them that way and these production problems have elegant solutions.
Running agents in production taught me monitoring is everything. We treat each agent like a microservice - health checks, proper logging, the works. The game-changer was agent versioning. When something breaks, you can roll back specific agents without touching anything else. For communication issues, we created agent contracts that define input/output schemas. Teams can’t deploy changes that break these contracts without approval. Sounds bureaucratic, but it killed most integration problems. We optimized costs with agent pooling - multiple requests share the same instance when possible. Also added request queuing during peak times instead of immediately spinning up new instances. The monitoring gap is real. We built custom dashboards tracking agent performance alongside business outcomes. Agent A having 95% uptime means nothing if it’s making bad decisions that tank conversion rates. Start simple with single agents and add complexity gradually. Most teams try building distributed agent systems too early and create unnecessary headaches.
Infrastructure complexity kills most agent deployments. Everyone obsesses over the AI part but ignores that production reliability is really about execution flow management.
I ditched thinking about this as a deployment problem and started treating it like workflow orchestration. Agents become nodes in automated workflows instead of standalone services that fight each other.
This instantly fixes your communication headaches. No more custom protocols or broken standards between teams. Agents pass data through defined workflow connections that automatically enforce structure.
For scaling, individual agents run when needed rather than spinning up full applications. Way better cost control since you’re not paying for idle infrastructure.
The tracking issue vanishes because every workflow execution logs the complete path. When something breaks, you see exactly which agent failed and can replay from that point.
Multi-agent coordination becomes trivial when they’re workflow steps instead of distributed services. No containers to manage, no service discovery, no network calls timing out.
We moved our entire agent system this way and cut deployment complexity by 80%. Instead of managing kubernetes clusters and message queues, everything runs through one automation platform.
Your team can focus on agent logic instead of fighting infrastructure problems. The tooling already exists for this approach.