Three ways to build AI agents for Splunk

Building AI agents shouldn’t require guesswork, hidden defaults, or rigid workflows.

Teams need different levels of control at different times — from fast experimentation to precise, production‑ready configuration. What matters is confidence: knowing how an agent was built, what it can access, and why it behaves the way it does.

In this demo, we show how Deslicer AI makes agent creation transparent, flexible, and fast, allowing teams to move from idea to live agent in minutes — without sacrificing control.

Three paths to the same outcome

Deslicer AI supports multiple ways to build an agent, depending on how much guidance or control you need.

Build through chat

Describe the agent in plain language. Deslicer selects the right tools, writes the prompt, and ships a guarded agent. You can review the system prompt, enabled tools, and immediately start interacting with the agent.

Build through a guided form

Use the step‑by‑step builder to define templates, behavior, tools, and constraints manually. Every field is visible. Nothing is skipped. Every decision is captured.

Clone from the marketplace

Start from any public agent, wire it to your own integrations and skills, and save. The agent is grounded, configured, and ready to run.

Three approaches. One launched agent.

What stays consistent — every time

Regardless of how the agent is created, Deslicer AI ensures:

• The full system prompt is visible and reviewable
• Enabled tools and skills are explicitly defined
• Agent behavior is transparent and auditable
• The agent is grounded in your integrations and data

This consistency is what allows teams to move fast without losing trust.

Why agent creation needs this level of clarity

The challenge

AI agents are powerful — but opaque setups create risk. When prompts, tools, or assumptions are hidden, teams lose confidence and struggle to scale usage responsibly.

The Deslicer approach

Deslicer AI treats agent creation as a first‑class, inspectable process. Whether you move fast through chat or fine‑tune every parameter, the outcome is the same: a clear, well‑defined agent you can trust.

The result

• Minutes from idea to live agent
• Flexibility for both beginners and power users
• Full visibility into how agents are built
• A solid foundation for safe, scalable automation

Who this is for

This use case is especially relevant for:

• Platform teams enabling agent‑driven workflows
• Organizations experimenting with AI in Splunk
• Power users who need full control over agent behavior
• Teams that want speed and governance