Agentic AI Playground
Watch AI work.
Give an enterprise agent a goal. Watch it think, retrieve, act and verify — and watch the policy engine allow, deny or escalate each action before it happens.
GOAL
> Production API latency has increased 35%.
AGENT STATE
MEMORY
Empty — the agent has not learned anything yet.
ACTIONS
Press RUN AGENT. Every step below is the agent's real trace — the plan, the tool call, the policy decision and the verification. Nothing is hidden, because in production nothing should be.
TOOLS
- Observability
- ITSM
- Cloud
- Database
- Runbooks
Mock systems only. The public playground never reaches a real customer system or a production credential.
GOVERNANCE
No decisions yet.
COST
Every agent run carries a unit cost. If you cannot see it here, you cannot see it in production either.
What you are looking at
Six Surfaces of Unified Truth
An agent that only shows you its answer is asking for trust it has not earned. These are the surfaces AgentOps exists to provide.
01
Goal
What the agent was asked to do, in the user's words — every later step must be justified against it.
02
Agent state
Planning, working, waiting or completed. A state machine you can supervise.
03
Memory
The context the agent holds and its learning scope in this run, recalled whenever needed.
04
Tools
The enterprise systems AI can reach and call; the tool list defines the boundary of its privilege.
05
Actions
Every step has its reason to show. When something goes wrong this is the trace that lets you reconstruct why.
06
Governance
Allowed, denied, approval required or escalated — evaluated before each action and recorded with its rationale.
Questions
AI Playground, Answered Directly
Is this a real agent or a recording?
The traces are scripted representations of real agent runs against mock enterprise systems. That is deliberate: a public playground wired to live systems would need production credentials, and no enterprise should accept that. What is real is the shape — the plan, the tool calls, the policy decisions, the verification step and the cost.
Why show denied and escalated actions?
Because that is what separates a governed agent from an impressive one. Any demo can show an agent succeeding. What matters in production is what happens when the agent wants to do something it should not be allowed to do — and whether the refusal is enforced by architecture or by hoping the model behaves.
How does an agent earn more autonomy?
Per action class, on evidence. The agent starts in shadow mode with its recommendation compared against what the engineer actually did. Once accuracy is demonstrated on a class of action, that class is promoted to act-with-approval, and only then to autonomous. Autonomy is never granted to an agent wholesale.
What does it cost to run agents like this?
The cost panel shows tokens and inference cost per run, which is the figure most programmes never measure. The number that matters commercially is cost per completed business outcome — including retries and failed attempts — because that is the only figure comparable against doing the work another way.
Choose your starting point
Where Are You Today?
Enterprise AI conversations stall when the entry point is wrong. Pick the sentence that matches your situation.
We have an idea.
→ AI STRATEGY
We have a POC.
→ AI PRODUCTIONIZATION
We have an AI application.
→ LLMOPS / AGENTOPS
We need autonomous workflows.
→ AGENTIC AI
Our AI is too expensive.
→ AI FINOPS
Our AI can't reach enterprise knowledge.
→ RAG & KNOWLEDGE
We can't send data to public AI.
→ SOVEREIGN AI
We need to run AI securely.
→ AI SECURITY
Now, Design One for Your Enterprise
Knowing which of your workflows has the telemetry, the tools, and the repeatability to support it with the policy envelope right from day one.
30 MINUTES WITH AN ARCHITECT · BRING YOUR HARDEST CONSTRAINT