You deployed eleven agents this year. Name the one that is failing right now.
AI-BUS Agent Monitoring is a single pane of glass for subscribing to and observing every AI agent running across your organization. Live status, health metrics, configuration detail and streamed execution logs — across every environment, with inline control actions. Read-heavy and action-light by design: it watches agent state without orchestrating agent logic.
AI-BUS Agent Monitoring — Organizational Agent Observability
The problem underneath.
Agents proliferate faster than the discipline around them. A team ships one, another team ships three, a vendor delivers two more, and within a year nobody can produce a list of what is running, in which environment, against which model. The first time this becomes visible is usually an outage that nobody noticed for six hours.
Traditional APM does not fill the gap. It tells you a service responded in 240ms; it does not tell you which LLM the agent called, which MCP servers it has mounted, which function-calling tools are registered, or which RAG knowledge base grounded the answer. Those are the things that actually break, and they are invisible to infrastructure monitoring.
The deliberate scope limit here matters. This is an observability layer, not an orchestrator. It does not own agent logic, does not sit in the execution path, and cannot become a single point of failure for the agents it watches. It observes, it streams, and it offers start, stop, restart and disable — nothing more, which is precisely why it can be trusted in production.
Input, decision, output.
The full path a request takes through AI-BUS Agent Monitoring — nothing hidden in the middle.
Agent registration
Every deployed agent subscribes; the platform maintains the live inventory.
WebSocket + SSE streams
Status events over WebSocket, execution logs over server-sent events, with polling fallback.
Single pane of glass
Card grid for scanning or list view for dense comparison, filtered by environment.
Agent detail panel
Models, MCP servers, tools and knowledge bases in one slide-over.
Inline control
Start, stop, restart or disable from the card, row or panel — no context switch.
What it actually does.
Real-time status
Live Running, Fault, Maintenance, Stopped and Restarting counts, pushed over WebSocket with polling fallback.
Multi-environment view
Filter agents across DEV, SIT, UAT and PROD — card grid to scan, list view to compare.
Agent detail panel
LLM models, MCP servers, function-calling tools and RAG knowledge bases per agent, in one slide-over.
Live log stream
Server-sent execution logs with type filters for model, function, MCP and error, plus expandable JSON payloads.
Inline actions
Start, stop, restart or disable any agent directly from the card, list row or detail panel.
Fault notification banner
A fault raised anywhere surfaces instantly in a top banner with agent, environment and error summary.
Who this is for — and who it isn't.
Most vendors only answer the first half. The second half saves everybody a quarter.
A good fit if
- You have more than a handful of agents in production and no single inventory of them
- Your agents call multiple models, MCP servers or RAG sources that can each fail independently
- You run several environments and need to compare behaviour across them
- You need role separation between people who can watch and people who can act
Probably not if
- You have one or two agents and a Slack alert is genuinely sufficient
- You are looking for an orchestration platform — this deliberately does not orchestrate
- You need deep token-level LLM tracing and evaluation rather than operational observability
Questions we get asked.
How is this different from Langfuse, LangSmith or standard APM?
Tracing tools focus on the quality of a single agent's reasoning — prompts, tokens, evaluations. APM focuses on infrastructure. This sits between them, at the operational layer: what is deployed, where, in what state, calling what — and can I restart it. Most organizations end up needing both.
Does it sit in the agent execution path?
No, and that is a deliberate constraint. Agents publish status and logs to the platform; the platform does not proxy or intercept agent traffic. If the monitoring layer goes down, your agents keep running.
What controls the difference between watching and acting?
Two roles. MonitorAdmin has full action access; MonitorViewer is strictly read-only. The distinction is enforced on both the frontend and the backend, so a read-only user cannot reach an action endpoint directly.
Which agent frameworks does it support?
Registration is framework-agnostic — an agent reports its identity, environment, models, MCP servers, tools and knowledge bases through a thin client. Agents built on different stacks appear side by side in the same view.
What happens when a fault is raised?
It surfaces immediately in a top banner naming the agent, its environment and the error summary, and the agent's card moves to Fault status. The execution log for that agent is one click away, filtered to errors.
Often deployed alongside.
Dispatcher Agent
Dispatcher Agent is an autonomous AI agent that monitors your company's dedicated email addresses, reads both the message and its attachments, decides which business process it belongs to, and starts that process in your CRM or ERP — or completes the action directly.
Meeting Intelligence Assistant
Meeting Intelligence Assistant takes Microsoft Teams transcripts, strips confidential content, passes them through Azure AI Content Safety, then chunks and indexes them in Azure AI Search with date and participant metadata.
HR AI Assistants
HR AI Assistants is a suite of seven specialist assistants sitting behind a single agentic orchestrator.
See AI-BUS Agent Monitoring on your own data.
Start with a free 90-minute assessment. We map where this fits in your organization, what it would touch, and what it would return — then you decide.