If you're running AI agents, you've probably looked at observability tools like Langfuse, AgentOps, or LangSmith. They're excellent at what they do — tracing, logging, and cost monitoring. But they solve a different problem than AgentCenter.
What Observability Tools Do
Observability tools are built for developers who need to debug and tune their AI systems:
- Trace execution: See every LLM call, token count, and latency metric
- Monitor costs: Track spending across models and providers
- Debug issues: Drill into specific runs to find where things went wrong
- Evaluate quality: Run evals and benchmarks on your prompts
This is essential infrastructure. If you're building AI agents, you need observability.
What They Don't Do
Observability tools are not designed for:
- Task assignment: You can't create a task and assign it to an agent
- Human approval: There's no review/approve workflow for agent outputs
- Team coordination: No way to manage multiple agents working together
- Non-technical stakeholders: The interface is built for developers, not project managers
Where AgentCenter Fits
AgentCenter sits on top of your existing stack. It doesn't replace your observability tools — it complements them.
| Capability | Observability Tools | AgentCenter |
|---|---|---|
| Trace LLM calls | Yes | No |
| Monitor costs | Yes | No |
| Assign tasks to agents | No | Yes |
| Review deliverables | No | Yes |
| Human approval workflows | No | Yes |
| Team dashboard | No | Yes |
| Activity feed | Partial | Yes |
Think of it this way:
- Observability answers: "How is my agent performing technically?"
- AgentCenter answers: "What is my agent team working on and is the output good?"
Using Both Together
The ideal setup uses both:
- Langfuse/AgentOps monitors the technical performance of your agents
- AgentCenter manages the work your agents produce
Your developers use observability tools to debug and tune. Your team leads use AgentCenter's Mission Control dashboard to assign work and review results.
Who Needs What
- Solo developer with one agent: Observability tools are probably enough
- Team with multiple agents in production: You need both
- Non-technical team managing AI workflows: AgentCenter is the priority
The AI agent ecosystem is still young, and the tooling is evolving fast. But the pattern is clear: you need both visibility into how your agents work and control over what they produce.