The Control Plane for AI Agents
Understand how autonomous agents reason, act, and interact with your systems in real time.
AI agents create dynamic execution paths across models, tools, APIs, and data. Traditional observability tools cannot explain these behaviors. Aither integrates with OpenClaw and other agent runtimes to visualize and model how agents actually operate.
Runtime graph
Live topology across agents, models, tools, APIs, and systems.
Execution tracing
Replay dynamic reasoning and multi-step tool workflows.
Governance
Understand cost, access, and anomalies across agent fleets.
Execution Graph
Non-deterministic runtime path reconstructed from live agent events.
Active agents
12
Tool calls
438 / hour
Model turns
1,902
Latest Events
Live ticker
browser.search
stripe.charge.lookup
slack.postMessage
llm.completion
Autonomous software creates invisible systems
Traditional infrastructure follows predictable request paths. Agent systems compose prompts, model calls, tools, APIs, and handoffs dynamically at runtime, which makes the execution graph change every run.
Traditional Infrastructure
Deterministic and well understood. Existing monitoring assumes this shape.
Agent Systems
Paths branch, loop, and change dynamically based on model output and tool results.
Why this breaks traditional observability
Traditional observability tools monitor deterministic infrastructure. AI agents introduce dynamic execution graphs and non-deterministic decision paths. This platform makes those behaviors observable.
Operators cannot understand why agents made decisions
Runtime context is fragmented across prompts, models, tools, and APIs unless it is captured as one execution graph.
Tool calls create hidden data access paths
Runtime context is fragmented across prompts, models, tools, and APIs unless it is captured as one execution graph.
Token usage and cost explode without visibility
Runtime context is fragmented across prompts, models, tools, and APIs unless it is captured as one execution graph.
Agent interactions become complex multi-system workflows
Runtime context is fragmented across prompts, models, tools, and APIs unless it is captured as one execution graph.
Visualize agent behavior
The platform builds a live execution map of autonomous software so platform teams can inspect what happened, why it happened, what it cost, and which systems were involved.
Runtime Agent Graph
Visualize agents, tools, APIs, models, and data systems as one connected execution graph.
Runtime Agent Graph
Decision Path Tracing
Replay agent reasoning steps, prompts, tool calls, and downstream effects with runtime context.
Decision Path Tracing
Agent Cost Intelligence
Track token usage and cost per workflow, per agent, and per tool invocation.
Agent Cost Intelligence
Blast Radius Mapping
Understand which systems an agent can reach across tools, APIs, data stores, and identities.
Blast Radius Mapping
Execution Timeline
Step through every run from initial prompt to terminal action in exact event order.
Execution Timeline
Built for OpenClaw agents
OpenClaw provides powerful autonomous agents capable of reasoning, tool use, and multi-agent coordination. Aither acts as the control plane above those systems, capturing reasoning steps, tool execution, and runtime interactions.
OpenClaw agents emit runtime events
Events are streamed into the control plane
The system builds a real-time graph of agent behavior
OpenClaw Agents
Autonomous agents reasoning, planning, and coordinating.
Instrumentation Layer
Runtime hooks emit prompts, tool calls, and decisions.
Control Plane
Events are normalized into a real-time execution model.
Visual Agent System Graph
Operators inspect dynamic paths, costs, and alerts.
Understand and govern autonomous systems
The control plane is not just for visibility. It gives teams a way to detect abnormal behavior, monitor tool access, audit actions, and identify risky execution paths before they become incidents.
Runtime Governance Console
Signals surfaced from live execution paths and policy context.
FinanceAgent calling payment API repeatedly
14 repeated `stripe.refund.create` calls across a single execution path.
Token usage spike
ResearchAgent exceeded expected context budget by 2.8x over the last 15 minutes.
Unauthorized data query
WriterAgent attempted `warehouse.customers.export` without matching policy scope.
Designed for agent engineers
Instrument local agents, stream runtime events, and feed the control plane with the data needed to understand autonomous systems in production.
Local agent instrumentation
OpenClaw runtime integration
Event streaming API
SDK for agent frameworks
Example event stream
These runtime events power the control plane.
{
agent: "ResearchAgent",
event: "tool_call",
tool: "browser.search",
latency: 480,
tokens: 1320
}
event.ingest
112k events / min
runtime.sdk
OpenClaw + custom frameworks
trace.rebuild
Execution graph updated in 1.2s
The operating system for autonomous software
As companies deploy thousands of AI agents across departments, they will need a control plane to understand and manage them. Just as cloud infrastructure required observability platforms, autonomous software requires a new layer of runtime visibility.
Agent behavior
Why a run branched, retried, or escalated.
Agent cost
Where tokens and tool usage accumulate.
Agent permissions
Which systems and APIs each workflow can reach.
Agent interactions
How agents coordinate across tools and models.
See your agents in action
Connect your OpenClaw runtime and watch your agent system come alive.