AgentUI
A user interface layer that surfaces agent actions, context, and controls so humans can supervise, steer, or approve runs.
AgentUI provides a window into agent behavior—showing tool calls, context, drafts, and decision points—so humans can monitor and intervene when needed.
In operations, AgentUI is used for review queues, inline approvals, and real-time debugging of agent runs. It reduces black-box risk by exposing what the agent is doing and why.
It fits between agents and operators: capturing traces, exposing controls (approve, edit, rerun), and summarizing state. The benefit is trust, faster debugging, and safer rollouts of agentic workflows.
Frequently Asked Questions
What should an AgentUI display?
Current step, tool calls with inputs/outputs, retrieved context, draft outputs, confidence/flags, and next planned action.
How do humans intervene via AgentUI?
Approve or reject steps, edit prompts/outputs, rerun with fixes, or route to fallback flows. Changes should be logged and auditable.
How does AgentUI improve safety?
By making decisions visible, enabling quick stops, and requiring approval for risky actions. It reduces unnoticed bad writes or comms.
Can AgentUI show cost and latency?
Yes—surface token/step costs and timings. Helps operators tune routes and models for budget and SLA.
What logs should AgentUI keep?
Full trace of steps, edits, approvals, and overrides with timestamps and user IDs. Enable replay for debugging.
Does AgentUI slow execution?
Only when human approval is required. For auto paths, keep UI read-only; for gated steps, show SLA timers and batched review queues.
How do I design AgentUI for clarity?
Use plain summaries, collapsible traces, and highlight risky actions. Keep controls consistent and avoid overloading reviewers.
Who should access AgentUI?
Operators and reviewers with proper roles. Restrict sensitive context and enforce RBAC for approvals.
Can AgentUI feed model improvement?
Yes—capture human corrections and apply them to prompts, rules, or training data. Track common fixes to prioritize improvements.
Agentic AI
An AI approach where models autonomously plan next steps, choose tools, and iterate toward an objective within guardrails.
Agentic Workflow
A sequence where an AI agent plans, executes tool calls, evaluates results, and loops until success criteria are met.
Agent Handoff
A pattern where one AI agent passes context and state to another specialized agent to keep multi-step automation modular.
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